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Using Google Satellite Embeddings to Predict JRC Forest Areas. Evaluate whether Google Earth satellite embeddings can effectively predict EC-JRC forest area labels, thereby testing the hypothesis that these embeddings capture meaningful ecological and land-cover information.
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GoogleEmbeddings_v2.ipynb
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"cell_type": "markdown",
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"source": [
"# 🌍 Using Google Satellite Embeddings to Predict JRC Forest Areas\n",
"\n",
"## 🎯 Objective\n",
"Test the hypothesis that **Google satellite embeddings** (GE) can predict **EC-JRC forest area labels**, validating their ecological information content.\n",
"\n",
"---\n",
"\n",
"## 🧩 Core Project Steps (Updated)\n",
"\n",
"### 1. Define Study Area & Sampling Blocks\n",
"- 🔄 **Select a specific pilot area.** The center point was chosen for Ebolowa, Cameroon, for its recognizable geography and land-use heterogeneity.\n",
"- ✅ Construct a **100 km × 100 km** study window around it.\n",
"- ✅ Tile into **5 km × 5 km blocks** → 20×20 = **400 blocks** total.\n",
"- ✅ Assign a unique `block_id` to each block.\n",
"\n",
"### 2. Sample Pixels Within Each Block\n",
"- 🔄 **Filter blocks for balanced classes.** Before sampling, blocks were analyzed, and only those with a minimum number of non-forest pixels (≥25) were selected for sampling to ensure viable training data.\n",
"- ✅ Sample up to **150 pixels inside** JRC forest and **150 outside** JRC forest per block.\n",
"- ✅ **Respect data availability.** The \"fill\" option was disabled, so blocks with fewer than 150 pixels in a class were not artificially balanced.\n",
"- ✅ Enforce **≥100 m minimum distance** between sampled points by sampling on a 100m grid.\n",
"- ✅ Record sampling metadata: `n_in_actual`, `filled_flag`, `block_id`, `x,y`.\n",
"\n",
"### 3. Extract Datasets from Google Earth Engine\n",
"- ✅ **GE Embeddings:** Extracted 64-dimensional per-pixel vectors for the year 2020.\n",
"- ✅ **JRC Label:** Used the binary forest mask for 2020 as the ground truth target variable.\n",
"- 🔄 **Sentinel-1:** Processed VV and VH bands to compute temporal statistics (**mean, std, min, max**) after speckle filtering.\n",
"- 🔄 **Sentinel-2:** Applied a cloud mask (using Google's Cloud Score Plus) and computed temporal statistics (**mean, median, robust amplitude**) for multiple vegetation indices (NDVI, EVI, NDWI, MSAVI2, SWIR/NIR).\n",
"- 🔄 **Export features.** All extracted features were combined and exported into single `.csv` files for each data source, rather than per block.\n",
"\n",
"### 4. Preprocess & Prepare for Modeling\n",
"- ✅ **Reproject all layers to 10 m** and a common CRS (handled by GEE during sampling).\n",
"- ✅ **Filter Sentinel-2 pixels** using a cloud probability threshold to ensure clear observations.\n",
"- 🔄 **Handle missing values.** Missing values in optical indices were handled using **imputation (mean/median)** during the modeling phase, not by setting them to `NaN` with a flag.\n",
"- 🆕 **Create spatial folds for cross-validation.** Unique block IDs were used to create 5 spatial groups (`GroupKFold`) to ensure the model is tested on geographically separate data.\n",
"\n",
"### 5. Define Models, Baselines, and Experiments\n",
"- 🔄 **Establish Baselines:** Used **k-Nearest Neighbors (kNN)** as the primary baseline model.\n",
"- 🆕 **Train Advanced Models:** Implemented and compared **Random Forest** and **XGBoost** classifiers.\n",
"- 🆕 **Conduct Comparative Experiments:** Systematically evaluated model performance on three distinct feature sets:\n",
" 1. **Test 1: E** (Embeddings only)\n",
" 2. **Test 2: I** (Indices only - S1 + S2)\n",
" 3. **Test 3: E+I** (Embeddings + Indices combined)\n",
"- 🆕 **Evaluate Performance:** Assessed models using multiple metrics suitable for classification: **AUC, PR-AUC, and Brier Score**.\n",
"\n",
"### 6. Hypothesis Testing and Interpretation\n",
"- 🆕 **Formally Test Hypothesis:** Used the modeling results to reject the null hypothesis, concluding that Google Satellite Embeddings contain strong predictive information for forest cover.\n",
"- 🆕 **Perform Geospatial Verification:** Trained a final model and used it to predict a forest map for an entire 5x5 km block, visually comparing the prediction to the ground truth.\n",
"- 🆕 **\"Probe the Black Box\":** Investigated what the embeddings learned by training regression models to predict Sentinel indices (e.g., NDVI) from the embeddings, revealing that key spectral information was already encoded.\n",
"\n",
"**Legend:**\n",
"- ✅ **Completed as planned.**\n",
"- 🔄 **Completed, but with changes** from the original plan.\n",
"- 🆕 **New step added** that was not in the original plan."
]
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"metadata": {
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},
"source": [
"## Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "zkuPieEyviaL",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 51381,
"status": "ok",
"timestamp": 1759947933687,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "zkuPieEyviaL",
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"# =============================================================================\n",
"# SETUP FOR GOOGLE COLAB\n",
"# =============================================================================\n",
"\n",
"# --- 1. Installation ---\n",
"# A flag to control whether installations run. Set to False if you're sure\n",
"# everything is already installed in your current session, or you are running\n",
"# the code locally.\n",
"RUN_INSTALL = True\n",
"\n",
"if RUN_INSTALL:\n",
" print(\"Installing required Python packages...\")\n",
" !pip install rasterio --quiet\n",
" !pip install rioxarray --quiet\n",
" !pip install geemap --quiet\n",
" !pip install xarray --quiet\n",
" !pip install dask --quiet\n",
" !pip install xee --quiet\n",
" \n",
" print(\"✅ Installation complete.\")\n",
"else:\n",
" print(\"Skipping package installation.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "51ba436b-fcfd-4446-bd61-465aca25ee0d",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 76,
"status": "ok",
"timestamp": 1759948189125,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "51ba436b-fcfd-4446-bd61-465aca25ee0d",
"outputId": "14e1c26c-e8eb-469b-cf08-3cecf7ac0784"
},
"outputs": [],
"source": [
"# ---------------------------------\n",
"# Core Data Handling & System\n",
"# ---------------------------------\n",
"import os\n",
"import math\n",
"import json\n",
"import random\n",
"import joblib\n",
"import numpy as np\n",
"import pandas as pd\n",
"import time\n",
"from tqdm.notebook import tqdm # For progress bars in notebooks\n",
"\n",
"# ---------------------------------\n",
"# Geospatial (Vector & Raster)\n",
"# ---------------------------------\n",
"import geopandas as gpd\n",
"from shapely.geometry import Polygon, Point, shape, box\n",
"from shapely.ops import unary_union, transform\n",
"import rasterio as rio\n",
"from rasterio.features import shapes\n",
"from rasterio.transform import from_origin\n",
"import rioxarray\n",
"import xarray as xr\n",
"import dask.array as da\n",
"\n",
"# ---------------------------------\n",
"# Google Earth Engine API\n",
"# ---------------------------------\n",
"import ee\n",
"import geemap\n",
"\n",
"# ---------------------------------\n",
"# Visualization\n",
"# ---------------------------------\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mpatches\n",
"import matplotlib.lines as mlines\n",
"from matplotlib.colors import ListedColormap, BoundaryNorm\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"import seaborn as sns\n",
"import folium\n",
"from branca.element import Figure as branca_figure\n",
"\n",
"# ---------------------------------\n",
"# Machine Learning\n",
"# ---------------------------------\n",
"# Preprocessing\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Models\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
"from sklearn.linear_model import LassoCV\n",
"from xgboost import XGBClassifier\n",
"\n",
"# Model Selection & Cross-Validation\n",
"from sklearn.model_selection import (\n",
" GroupKFold,\n",
" KFold,\n",
" StratifiedKFold,\n",
" train_test_split,\n",
" ParameterGrid\n",
")\n",
"\n",
"# Metrics & Evaluation\n",
"from sklearn.metrics import (\n",
" accuracy_score,\n",
" roc_auc_score,\n",
" average_precision_score,\n",
" brier_score_loss,\n",
" f1_score,\n",
" r2_score,\n",
" roc_curve,\n",
" precision_recall_curve\n",
")\n",
"\n",
"from IPython.core.display import HTML\n",
"table_css = 'table {align:left;display:block} '\n",
"HTML('<style>{}</style>'.format(table_css))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "824b1cae-3c7a-4724-9a25-7eb29c41af81",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"executionInfo": {
"elapsed": 1493,
"status": "ok",
"timestamp": 1759947946473,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "824b1cae-3c7a-4724-9a25-7eb29c41af81",
"outputId": "e840401d-a68e-4d31-c01f-aa3c5ae50b8b"
},
"outputs": [],
"source": [
"# IMPORTANT: Replace with your own Google Earth Engine Project ID\n",
"project_id = 'your-gee-project-id'\n",
"\n",
"# Initiating GEE:\n",
"try:\n",
" ee.Initialize(project=project_id)\n",
"except:\n",
" print(\"An exception occurred\")\n",
" try:\n",
" ee.Authenticate()\n",
"\n",
" # change this to the commercial licence:\n",
" ee.Initialize(project=project_id) # abeya-test-202508\n",
" except:\n",
" print(\"An second exception occurred\")\n",
"finally:\n",
" print(\"Initiating GEE is finished\")\n",
"\n",
" # Print the elevation of Mount Everest.\n",
" dem = ee.Image('USGS/SRTMGL1_003')\n",
" xy = ee.Geometry.Point([86.9250, 27.9881])\n",
" elev = dem.sample(xy, 30).first().get('elevation').getInfo()\n",
" print('Mount Everest elevation (m):', elev)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5dd97b7-33aa-4bfe-8d5a-4ab6758a6849",
"metadata": {
"executionInfo": {
"elapsed": 21,
"status": "ok",
"timestamp": 1759947946509,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "f5dd97b7-33aa-4bfe-8d5a-4ab6758a6849"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "58917bf1-08b1-4eef-a9b7-3694167b2f13",
"metadata": {
"id": "58917bf1-08b1-4eef-a9b7-3694167b2f13"
},
"source": [
"## Functions"
]
},
{
"cell_type": "markdown",
"id": "239f518b-5699-4ee9-a677-f6e2559190f0",
"metadata": {
"id": "239f518b-5699-4ee9-a677-f6e2559190f0",
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"### Part 1: Define Study Area & Sampling Blocks"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "caf06865-f724-4690-885a-4782a451beed",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 380,
"status": "ok",
"timestamp": 1759947946911,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "caf06865-f724-4690-885a-4782a451beed",
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"outputs": [],
"source": [
"# Define Study Area & Sampling Blocks\n",
"def lonlat_to_utm_epsg(lon, lat):\n",
" zone = int((lon + 180) / 6) + 1\n",
" return 32600 + zone if lat >= 0 else 32700 + zone\n",
"\n",
"def generate_centered_grid(x, y, tile_size, buffer_dist, crs):\n",
" n = 1\n",
" while (n * tile_size) / 2 < buffer_dist / 2:\n",
" n += 2 # Ensure odd: 1, 3, 5, ...\n",
" print(f\"Using {n}x{n} grid of {tile_size} m tiles\")\n",
"\n",
" half_extent = (n * tile_size) / 2\n",
" x_min = x - half_extent\n",
" y_min = y - half_extent\n",
"\n",
" # Generate tiles row-by-row (top to bottom)\n",
" tiles = []\n",
" ids = []\n",
" for row in range(n):\n",
" for col in range(n):\n",
" x0 = x_min + col * tile_size\n",
" y0 = y_min + row * tile_size\n",
" tile = box(x0, y0, x0 + tile_size, y0 + tile_size)\n",
" block_id = f\"{row:03d}-{col:03d}\"\n",
" tiles.append(tile)\n",
" ids.append(block_id)\n",
"\n",
" grid_gdf = gpd.GeoDataFrame({'block_id': ids, 'geometry': tiles}, crs=crs)\n",
" return grid_gdf\n",
"\n",
"def make_buffered_grid_wgs84_from_utm_bounds(gdf_utm, buffer_m=30):\n",
" \"\"\"\n",
" Buffers each polygon tile in UTM by `buffer_m` meters and transforms to WGS84.\n",
"\n",
" Parameters:\n",
" gdf_utm (GeoDataFrame): Grid in projected UTM CRS.\n",
" buffer_m (float): Buffer distance in meters.\n",
"\n",
" Returns:\n",
" GeoDataFrame: Buffered grid in WGS84.\n",
" \"\"\"\n",
" # Buffer each tile in-place\n",
" buffered_tiles = gdf_utm.copy()\n",
" buffered_tiles['geometry'] = buffered_tiles.geometry.buffer(buffer_m)\n",
"\n",
" # Transform to WGS84\n",
" gdf_wgs84 = buffered_tiles.to_crs(\"EPSG:4326\")\n",
"\n",
" return gdf_wgs84\n",
"\n",
"# Create the project extent for GEE\n",
"def create_gee_clip_extent_from_buffered_grid(grid_gdf_wgs84):\n",
" \"\"\"\n",
" Merges all buffered polygons and returns a GEE Geometry bounding box.\n",
"\n",
" Parameters:\n",
" grid_gdf_wgs84 (GeoDataFrame): Buffered grid polygons in WGS84.\n",
"\n",
" Returns:\n",
" ee.Geometry: Bounding rectangle as Earth Engine Geometry.\n",
" \"\"\"\n",
" # Merge all geometries\n",
" union_geom = unary_union(grid_gdf_wgs84.geometry)\n",
"\n",
" # Get bounding box of the union\n",
" bounds = union_geom.bounds # (minx, miny, maxx, maxy)\n",
"\n",
" # Convert to EE Geometry.Rectangle\n",
" clip_extent = ee.Geometry.Rectangle(list(bounds))\n",
"\n",
" return clip_extent\n",
"\n",
"\n",
"# Function to generate the project map\n",
"def create_aoi_map(boundary_gdf, zoom_start=10, tile_size=5000,\n",
" grid_snap=5120, buffer_m=30):\n",
" \"\"\"\n",
" Build a folium map with AOI, buffer, extent, and grid layers.\n",
"\n",
" Parameters\n",
" ----------\n",
" boundary_gdf : GeoDataFrame\n",
" Input AOI polygon(s) in WGS84.\n",
" tile_size : int\n",
" Tile size in meters (default 5000).\n",
" grid_snap : int\n",
" Grid snapping in meters (default 5120).\n",
" buffer_m : int\n",
" Buffer in meters for tile edges (default 30).\n",
"\n",
" Returns\n",
" -------\n",
" folium.Map\n",
" \"\"\"\n",
" # --- SETTINGS ---\n",
" crs_wgs84 = \"EPSG:4326\"\n",
" tile_size = 5000 # meters or 500px\n",
"\n",
" # --- 1. Convert AOI polygon to Earth Engine Geometry ---\n",
" boundary_geom = ee.Geometry.Polygon(boundary_gdf.geometry.iloc[0].exterior.coords[:])\n",
" AOI = boundary_geom\n",
" center = AOI.centroid(10).coordinates().reverse().getInfo()\n",
" lat, lon = center\n",
"\n",
" # --- 2. Get UTM EPSG code and create UTM projection ---\n",
" epsg_code = lonlat_to_utm_epsg(lon, lat)\n",
" print(f\"✅ UTM EPSG code: EPSG:{epsg_code}\")\n",
" utm_crs = f\"EPSG:{epsg_code}\"\n",
" utm_proj = ee.Projection(utm_crs).atScale(1)\n",
"\n",
" # --- 3. Project AOI to UTM and get buffered bounds ---\n",
" aoi_utm = AOI.transform(utm_proj, 1)\n",
" aoi_utm_buffered_bounds = aoi_utm.buffer(1).bounds(1).transform(utm_proj, 1)\n",
"\n",
" # --- 4. Get center UTM coordinates ---\n",
" center_coords = aoi_utm.centroid(1).coordinates().getInfo()\n",
" center_x, center_y = center_coords\n",
"\n",
" # --- 5. Convert center to GeoDataFrame in UTM ---\n",
" center_point = Point(center_x, center_y)\n",
" center_gdf_utm = gpd.GeoDataFrame(geometry=[center_point], crs=utm_crs)\n",
"\n",
" # --- 6. Extract boundary extents in UTM ---\n",
" coords = ee.List(aoi_utm_buffered_bounds.coordinates().get(0))\n",
" xs = coords.map(lambda pt: ee.Number(ee.List(pt).get(0)))\n",
" ys = coords.map(lambda pt: ee.Number(ee.List(pt).get(1)))\n",
" x_min = ee.Number(xs.reduce(ee.Reducer.min())).floor().divide(10).multiply(10)\n",
" x_max = ee.Number(xs.reduce(ee.Reducer.max())).ceil().divide(10).multiply(10)\n",
" y_min = ee.Number(ys.reduce(ee.Reducer.min())).floor().divide(10).multiply(10)\n",
" y_max = ee.Number(ys.reduce(ee.Reducer.max())).ceil().divide(10).multiply(10)\n",
"\n",
" dx = x_max.subtract(x_min)\n",
" dy = y_max.subtract(y_min)\n",
"\n",
" buffer_dist = dx.max(dy).add(10)\n",
" buffer_dist_py = buffer_dist.getInfo()\n",
" print(f\"Original buffer distance: {buffer_dist_py} m\")\n",
"\n",
" # --- 7. Snap to grid ---\n",
" grid_snap = 5120 #in meters\n",
" buffer_dist_rounded = math.ceil(buffer_dist_py / grid_snap) * grid_snap + 0.5 * grid_snap\n",
" print(f\"Snapped buffer distance: {buffer_dist_rounded} m\")\n",
"\n",
" # --- 8. Generate grid centered on AOI ---\n",
" # Create GeoDataFrame directly in WGS84\n",
" center_point = Point(center_x, center_y)\n",
" center_gdf = gpd.GeoDataFrame(geometry=[center_point], crs=\"EPSG:4326\")\n",
" center_gdf_utm = center_gdf.to_crs(f\"EPSG:{epsg_code}\")\n",
"\n",
" center_point_utm = center_gdf_utm.geometry.iloc[0]\n",
" utm_x = center_point_utm.x\n",
" utm_y = center_point_utm.y\n",
" utm_x = round(center_point_utm.x / 5) * 5\n",
" utm_y = round(center_point_utm.y / 5) * 5\n",
" grid_gdf = generate_centered_grid(utm_x, utm_y, tile_size, buffer_dist_rounded, utm_crs)\n",
"\n",
" # grid_gdf is in UTM (e.g., EPSG:32630)\n",
" buffered_grid_wgs84 = make_buffered_grid_wgs84_from_utm_bounds(grid_gdf, buffer_m=30)\n",
"\n",
" clip_extent_GEE = create_gee_clip_extent_from_buffered_grid(buffered_grid_wgs84)\n",
" clip_extent_geojson = clip_extent_GEE.getInfo() # ee.Geometry.Rectangle to GeoJSON dict\n",
" clip_extent_shape = shape(clip_extent_geojson) # shapely.geometry\n",
"\n",
" # Wrap into a GeoDataFrame for styling or projection if needed\n",
" clip_extent_gdf = gpd.GeoDataFrame(geometry=[clip_extent_shape], crs=\"EPSG:4326\")\n",
"\n",
" # --- 9. Create visual layers (converted to WGS84) ---\n",
" # a) AOI Polygon\n",
" aoi_gdf = gpd.GeoDataFrame(geometry=[shape(boundary_geom.getInfo())], crs=crs_wgs84)\n",
"\n",
" # b) Center Point\n",
" center_gdf = center_gdf_utm.to_crs(crs_wgs84)\n",
"\n",
" # c) Buffer Circle\n",
" buffer_circle = center_gdf_utm.geometry.iloc[0].buffer(buffer_dist_py / 2)\n",
" buffer_gdf = gpd.GeoDataFrame(geometry=[buffer_circle], crs=utm_crs).to_crs(crs_wgs84)\n",
"\n",
" # d) Buffer Extent (square)\n",
" extent_geom = shape(aoi_utm_buffered_bounds.getInfo())\n",
" extent_gdf = gpd.GeoDataFrame(geometry=[extent_geom], crs=utm_crs).to_crs(crs_wgs84)\n",
"\n",
" # e) Grid\n",
" grid_gdf = buffered_grid_wgs84\n",
" clip_extent = clip_extent_gdf\n",
"\n",
" # Plot centroids for first and last cell\n",
" # First and last cell\n",
" first_cell = grid_gdf.iloc[0]\n",
" last_cell = grid_gdf.iloc[-1]\n",
"\n",
" # --- 10. Create folium map ---\n",
" m = folium.Map(location=[lat, lon], zoom_start=zoom_start, tiles=\"CartoDB Positron\")\n",
"\n",
" folium.GeoJson(aoi_gdf, name=\"AOI (boundary)\", style_function=lambda x: {\n",
" \"color\": \"black\", \"weight\": 2, \"fillOpacity\": 0\n",
" }).add_to(m)\n",
"\n",
" folium.Marker([lat, lon], popup=\"AOI Center\", icon=folium.Icon(color=\"blue\")).add_to(m)\n",
"\n",
" folium.GeoJson(buffer_gdf, name=\"Buffer (circle)\", style_function=lambda x: {\n",
" \"color\": \"red\", \"weight\": 1, \"fillOpacity\": 0.05\n",
" }).add_to(m)\n",
"\n",
" folium.GeoJson(extent_gdf, name=\"Buffer extent\", style_function=lambda x: {\n",
" \"color\": \"orange\", \"weight\": 2, \"fillOpacity\": 0\n",
" }).add_to(m)\n",
"\n",
" folium.GeoJson(grid_gdf, name=\"Grid (tiles)\", style_function=lambda x: {\n",
" \"color\": \"green\", \"weight\": 0.5, \"fillOpacity\": 0\n",
" }).add_to(m)\n",
"\n",
" folium.GeoJson(clip_extent, name=\"Grid (extent)\", style_function=lambda x: {\n",
" \"color\": \"black\", \"weight\": 1, \"fillOpacity\": 0\n",
" }).add_to(m)\n",
"\n",
" # Add first cell polygon (red) on top\n",
" folium.GeoJson(first_cell.geometry,\n",
" name=f\"First Cell {first_cell.block_id}\",\n",
" style_function=lambda x: {\"color\": \"red\", \"weight\": 2, \"fillOpacity\": 0.1}).add_to(m)\n",
"\n",
" # Add last cell polygon (blue) on top\n",
" folium.GeoJson(last_cell.geometry,\n",
" name=f\"Last Cell {last_cell.block_id}\",\n",
" style_function=lambda x: {\"color\": \"blue\", \"weight\": 2, \"fillOpacity\": 0.1}).add_to(m)\n",
"\n",
" # Optional: add markers at centroids\n",
" folium.Marker([first_cell.geometry.centroid.y, first_cell.geometry.centroid.x],\n",
" popup=f\"First Cell: {first_cell.block_id}\",\n",
" icon=folium.Icon(color=\"red\")).add_to(m)\n",
"\n",
" folium.Marker([last_cell.geometry.centroid.y, last_cell.geometry.centroid.x],\n",
" popup=f\"Last Cell: {last_cell.block_id}\",\n",
" icon=folium.Icon(color=\"blue\")).add_to(m)\n",
"\n",
" folium.LayerControl().add_to(m)\n",
"\n",
" return buffered_grid_wgs84, clip_extent_GEE, m"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74d888c9-1bde-4957-9285-1d1e508f004c",
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1759947946938,
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"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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"user_tz": -120
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"id": "74d888c9-1bde-4957-9285-1d1e508f004c"
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"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "b8047fde-4fa7-4980-935b-55e40c721173",
"metadata": {
"id": "b8047fde-4fa7-4980-935b-55e40c721173",
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"### Part 2 Sampling Pixels"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca903100-2644-456d-98a2-f417a4e294e1",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 41,
"status": "ok",
"timestamp": 1759947947006,
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"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
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"id": "ca903100-2644-456d-98a2-f417a4e294e1",
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"source": [
"# Helper: create 100m grid points inside an ee.Geometry block\n",
"def generate_grid_points(block_geom, spacing=100):\n",
" # pixelLonLat and sample with a scale ~ spacing (meters)\n",
" lonlat = ee.Image.pixelLonLat()\n",
" pts = lonlat.sample(\n",
" region=block_geom,\n",
" scale=spacing,\n",
" projection=ee.Projection('EPSG:4326').atScale(spacing),\n",
" geometries=True\n",
" )\n",
" return pts\n",
"\n",
"def sample_jrc_on_grid(JRC_forest, block_geom, block_id, spacing=100, sample_scale=10):\n",
" \"\"\"\n",
" Sample the JRC forest map within one 5×5 km block on a regular 100 m grid.\n",
"\n",
" Parameters\n",
" ----------\n",
" JRC_forest : ee.Image\n",
" The pre-loaded JRC forest image (e.g. ee.ImageCollection('JRC/GFC2020/V2').mosaic().select('Map'))\n",
" block_geom : ee.Geometry\n",
" The block geometry (typically from GeoDataFrame row converted to ee.Geometry)\n",
" block_id : str or int\n",
" Unique block identifier\n",
" spacing : int\n",
" Distance between sample points in meters (default: 100)\n",
" sample_scale : int\n",
" Pixel resolution for sampling (default: 10 m)\n",
"\n",
" Returns\n",
" -------\n",
" ee.FeatureCollection\n",
" Points with 'forest' property (1 = forest, 0 = non-forest)\n",
" and metadata {block_id, x, y}\n",
" \"\"\"\n",
"\n",
" # --- 1. Generate regular grid of points (e.g., 100 m spacing)\n",
" grid_pts = generate_grid_points(block_geom, spacing=spacing)\n",
"\n",
" # --- 2. Clip and unmask only within block ---\n",
" #jrc_block = JRC_forest.clip(block_geom).unmask(0).rename('Forest')\n",
"\n",
" # 2.1. Create a zero image matching the clipped area\n",
" # --- 2.1 Create a zero image at the right scale and projection ---\n",
" # Use the JRC image projection\n",
" proj = JRC_forest.projection().atScale(sample_scale)\n",
"\n",
" # Create a zeros image\n",
" zeros = ee.Image(0).rename('Forest').reproject(proj)\n",
"\n",
" # Clip to the block\n",
" zeros_block = zeros.clip(block_geom)\n",
"\n",
" # Add the forest values\n",
" jrc_block = zeros_block.add(JRC_forest.clip(block_geom)).rename('Forest')\n",
"\n",
" # --- 3. Sample values from JRC forest map ---\n",
" samples = jrc_block.sampleRegions(\n",
" collection=grid_pts,\n",
" scale=sample_scale,\n",
" geometries=True\n",
" )\n",
"\n",
" # --- 4. Add metadata for traceability ---\n",
" samples = samples.map(lambda f:\n",
" f.set({\n",
" 'block_id': block_id,\n",
" 'x': f.geometry().coordinates().get(0),\n",
" 'y': f.geometry().coordinates().get(1)\n",
" })\n",
" )\n",
"\n",
" # print(jrc_block.projection().getInfo())\n",
" # print(jrc_block.getInfo())\n",
"\n",
" return samples, jrc_block\n",
"\n",
"def balance_samples(samples_gdf, field_name=None, n_per_class=250, block_id=None, fill=True):\n",
" \"\"\"\n",
" Balance sample points per class, optionally filling missing points.\n",
"\n",
" Parameters\n",
" ----------\n",
" samples_gdf : GeoDataFrame\n",
" Sampled points with a 'forest' column (0/1).\n",
" n_per_class : int\n",
" Number of points per class.\n",
" block_id : str, optional\n",
" Block identifier to attach.\n",
" fill : bool\n",
" If True, fill missing points with opposite class (with replacement).\n",
" If False, just use available points.\n",
"\n",
" Returns\n",
" -------\n",
" balanced_gdf : GeoDataFrame\n",
" Balanced sample points with metadata.\n",
" \"\"\"\n",
" balanced_list = []\n",
"\n",
" for cls in [1, 0]:\n",
" cls_points = samples_gdf[samples_gdf[field_name]==cls]\n",
" n_actual = len(cls_points)\n",
"\n",
" #print(f\"n_actual {n_actual}\")\n",
"\n",
" if n_actual >= n_per_class:\n",
" selected = cls_points.sample(n=n_per_class, random_state=42)\n",
" filled_flag = False\n",
" else:\n",
" selected = cls_points.copy()\n",
" filled_flag = False\n",
" if fill:\n",
" n_fill = n_per_class - n_actual\n",
" fill_class = 1 - cls\n",
" fill_points = samples_gdf[samples_gdf[field_name]==fill_class].sample(\n",
" n=n_fill, replace=True, random_state=42\n",
" )\n",
" fill_points = fill_points.copy()\n",
" fill_points[field_name] = cls # mark as filled to match class\n",
" selected = pd.concat([selected, fill_points], ignore_index=True)\n",
" filled_flag = True\n",
" else:\n",
" # Take all available points, do NOT fill\n",
" selected = cls_points.copy()\n",
" filled_flag = False\n",
"\n",
" selected['n_in_actual'] = n_actual\n",
" selected['filled_flag'] = filled_flag\n",
"\n",
" if block_id is not None:\n",
" selected['block_id'] = block_id\n",
"\n",
" balanced_list.append(selected)\n",
"\n",
" balanced_gdf = pd.concat(balanced_list, ignore_index=True)\n",
" return balanced_gdf"
]
},
{
"cell_type": "markdown",
"id": "9e671a04-39f7-492f-bd89-b4d677ca3351",
"metadata": {
"id": "9e671a04-39f7-492f-bd89-b4d677ca3351",
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"### Part 3 - Modeling Functions"
]
},
{
"cell_type": "markdown",
"id": "bc595b39-6453-4d75-8585-fe980170919c",
"metadata": {
"id": "bc595b39-6453-4d75-8585-fe980170919c"
},
"source": [
"#### kNN with GroupKFold cross-validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c9355eb-01df-4ba5-bb49-c41325582887",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 28,
"status": "ok",
"timestamp": 1759947947038,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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"user_tz": -120
},
"id": "3c9355eb-01df-4ba5-bb49-c41325582887",
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"source": [
"def knn_group_cv(df, feature_cols, target_col, group_col='block_id', n_splits=5,\n",
" n_neighbors=5, plot_curves=False):\n",
" \"\"\"\n",
" Run kNN with GroupKFold cross-validation and compute metrics.\n",
"\n",
" Parameters:\n",
" -----------\n",
" df : pd.DataFrame\n",
" Input data including features, target, and grouping column.\n",
" feature_cols : list\n",
" List of feature column names to use.\n",
" target_col : str\n",
" Target column name (binary).\n",
" group_col : str\n",
" Column to use for GroupKFold (spatial blocks).\n",
" n_splits : int\n",
" Number of folds for GroupKFold.\n",
" n_neighbors : int\n",
" k for kNN classifier.\n",
" plot_curves : bool\n",
" If True, plots ROC and PR curves per fold.\n",
"\n",
" Returns:\n",
" --------\n",
" results_df : pd.DataFrame\n",
" Metrics per fold (AUC, PR_AUC, Brier).\n",
" mean_metrics : pd.Series\n",
" Average metrics across folds.\n",
" \"\"\"\n",
" from sklearn.model_selection import GroupKFold\n",
" gkf = GroupKFold(n_splits=n_splits)\n",
" splits = list(gkf.split(X=df, y=df[target_col], groups=df[group_col]))\n",
"\n",
" results_list = []\n",
"\n",
" plt.figure(figsize=(12,5)) if plot_curves else None\n",
"\n",
" for fold, (train_idx, test_idx) in enumerate(splits):\n",
" X_train = df.loc[train_idx, feature_cols].values\n",
" y_train = df.loc[train_idx, target_col].values\n",
" X_test = df.loc[test_idx, feature_cols].values\n",
" y_test = df.loc[test_idx, target_col].values\n",
"\n",
" knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
" knn.fit(X_train, y_train)\n",
" y_prob = knn.predict_proba(X_test)[:,1]\n",
"\n",
" auc = roc_auc_score(y_test, y_prob)\n",
" pr_auc = average_precision_score(y_test, y_prob)\n",
" brier = brier_score_loss(y_test, y_prob)\n",
"\n",
" results_list.append({'fold': fold, 'AUC': auc, 'PR_AUC': pr_auc, 'Brier': brier})\n",
" print(f\"Fold {fold}: AUC={auc:.3f}, PR_AUC={pr_auc:.3f}, Brier={brier:.3f}\")\n",
"\n",
" if plot_curves:\n",
" # ROC curve\n",
" fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
" plt.subplot(1,2,1)\n",
" plt.plot(fpr, tpr, label=f'Fold {fold}')\n",
" plt.xlabel('FPR'); plt.ylabel('TPR'); plt.title('ROC Curves'); plt.legend()\n",
" # Precision-Recall curve\n",
" precision, recall, _ = precision_recall_curve(y_test, y_prob)\n",
" plt.subplot(1,2,2)\n",
" plt.plot(recall, precision, label=f'Fold {fold}')\n",
" plt.xlabel('Recall'); plt.ylabel('Precision'); plt.title('PR Curves'); plt.legend()\n",
"\n",
" results_df = pd.DataFrame(results_list)\n",
" mean_metrics = results_df[['AUC','PR_AUC','Brier']].mean()\n",
"\n",
" if plot_curves:\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" print(\"\\nAverage metrics across folds:\")\n",
" print(mean_metrics)\n",
"\n",
" return results_df, mean_metrics\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "e0af55bc-8011-4664-8f1a-2317f883ff85",
"metadata": {
"id": "e0af55bc-8011-4664-8f1a-2317f883ff85"
},
"source": [
"#### Random Forest with GroupKFold cross-validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "205c4742-4006-4cee-b0a0-b304b8ea6643",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 70,
"status": "ok",
"timestamp": 1759947947112,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
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"source": [
"def rf_group_cv(df, feature_cols, target_col, group_col='block_id', n_splits=5,\n",
" n_estimators=200, max_depth=None, random_state=42, plot_curves=False):\n",
" \"\"\"\n",
" Run Random Forest with GroupKFold cross-validation and compute metrics.\n",
"\n",
" Parameters:\n",
" -----------\n",
" df : pd.DataFrame\n",
" Input data including features, target, and grouping column.\n",
" feature_cols : list\n",
" List of feature column names to use.\n",
" target_col : str\n",
" Target column name (binary).\n",
" group_col : str\n",
" Column to use for GroupKFold (spatial blocks).\n",
" n_splits : int\n",
" Number of folds for GroupKFold.\n",
" n_estimators : int\n",
" Number of trees in the Random Forest.\n",
" max_depth : int or None\n",
" Maximum tree depth.\n",
" random_state : int\n",
" Random seed for reproducibility.\n",
" plot_curves : bool\n",
" If True, plots ROC and PR curves per fold.\n",
"\n",
" Returns:\n",
" --------\n",
" results_df : pd.DataFrame\n",
" Metrics per fold (AUC, PR_AUC, Brier).\n",
" mean_metrics : pd.Series\n",
" Average metrics across folds.\n",
" \"\"\"\n",
" from sklearn.model_selection import GroupKFold\n",
" gkf = GroupKFold(n_splits=n_splits)\n",
" splits = list(gkf.split(X=df, y=df[target_col], groups=df[group_col]))\n",
"\n",
" results_list = []\n",
"\n",
" plt.figure(figsize=(12,5)) if plot_curves else None\n",
"\n",
" for fold, (train_idx, test_idx) in enumerate(splits):\n",
" X_train = df.loc[train_idx, feature_cols].values\n",
" y_train = df.loc[train_idx, target_col].values\n",
" X_test = df.loc[test_idx, feature_cols].values\n",
" y_test = df.loc[test_idx, target_col].values\n",
"\n",
" rf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth,\n",
" random_state=random_state, n_jobs=-1)\n",
" rf.fit(X_train, y_train)\n",
" y_prob = rf.predict_proba(X_test)[:,1]\n",
"\n",
" auc = roc_auc_score(y_test, y_prob)\n",
" pr_auc = average_precision_score(y_test, y_prob)\n",
" brier = brier_score_loss(y_test, y_prob)\n",
"\n",
" results_list.append({'fold': fold, 'AUC': auc, 'PR_AUC': pr_auc, 'Brier': brier})\n",
" print(f\"Fold {fold}: AUC={auc:.3f}, PR_AUC={pr_auc:.3f}, Brier={brier:.3f}\")\n",
"\n",
" if plot_curves:\n",
" # ROC curve\n",
" fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
" plt.subplot(1,2,1)\n",
" plt.plot(fpr, tpr, label=f'Fold {fold}')\n",
" plt.xlabel('FPR'); plt.ylabel('TPR'); plt.title('ROC Curves'); plt.legend()\n",
" # Precision-Recall curve\n",
" precision, recall, _ = precision_recall_curve(y_test, y_prob)\n",
" plt.subplot(1,2,2)\n",
" plt.plot(recall, precision, label=f'Fold {fold}')\n",
" plt.xlabel('Recall'); plt.ylabel('Precision'); plt.title('PR Curves'); plt.legend()\n",
"\n",
" results_df = pd.DataFrame(results_list)\n",
" mean_metrics = results_df[['AUC','PR_AUC','Brier']].mean()\n",
"\n",
" if plot_curves:\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" print(\"\\nAverage metrics across folds:\")\n",
" print(mean_metrics)\n",
"\n",
" return results_df, mean_metrics\n"
]
},
{
"cell_type": "markdown",
"id": "5409fa98-404d-4894-9dfb-2aa307ab430e",
"metadata": {
"id": "5409fa98-404d-4894-9dfb-2aa307ab430e"
},
"source": [
"#### XGBoost with GroupKFold cross-validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "696a1f6c-77e7-421b-88f5-80cee6f9bb71",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
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"executionInfo": {
"elapsed": 84,
"status": "ok",
"timestamp": 1759947947199,
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"source": [
"def xgb_group_cv(df, feature_cols, target_col, group_col='block_id', n_splits=5,\n",
" n_estimators=200, max_depth=6, learning_rate=0.1, random_state=42,\n",
" plot_curves=False):\n",
" \"\"\"\n",
" Run XGBoost with GroupKFold cross-validation and compute metrics.\n",
"\n",
" Parameters:\n",
" -----------\n",
" df : pd.DataFrame\n",
" Input data including features, target, and grouping column.\n",
" feature_cols : list\n",
" List of feature column names to use.\n",
" target_col : str\n",
" Target column name (binary).\n",
" group_col : str\n",
" Column to use for GroupKFold (spatial blocks).\n",
" n_splits : int\n",
" Number of folds for GroupKFold.\n",
" n_estimators : int\n",
" Number of boosting rounds.\n",
" max_depth : int\n",
" Maximum tree depth.\n",
" learning_rate : float\n",
" Step size shrinkage.\n",
" random_state : int\n",
" Random seed for reproducibility.\n",
" plot_curves : bool\n",
" If True, plots ROC and PR curves per fold.\n",
"\n",
" Returns:\n",
" --------\n",
" results_df : pd.DataFrame\n",
" Metrics per fold (AUC, PR_AUC, Brier).\n",
" mean_metrics : pd.Series\n",
" Average metrics across folds.\n",
" \"\"\"\n",
" from sklearn.model_selection import GroupKFold\n",
" gkf = GroupKFold(n_splits=n_splits)\n",
" splits = list(gkf.split(X=df, y=df[target_col], groups=df[group_col]))\n",
"\n",
" results_list = []\n",
"\n",
" plt.figure(figsize=(12,5)) if plot_curves else None\n",
"\n",
" for fold, (train_idx, test_idx) in enumerate(splits):\n",
" X_train = df.loc[train_idx, feature_cols].values\n",
" y_train = df.loc[train_idx, target_col].values\n",
" X_test = df.loc[test_idx, feature_cols].values\n",
" y_test = df.loc[test_idx, target_col].values\n",
"\n",
" xgb = XGBClassifier(\n",
" n_estimators=n_estimators,\n",
" max_depth=max_depth,\n",
" learning_rate=learning_rate,\n",
" random_state=random_state,\n",
" eval_metric='logloss', # keep this\n",
" n_jobs=-1\n",
" )\n",
" xgb.fit(X_train, y_train)\n",
" y_prob = xgb.predict_proba(X_test)[:,1]\n",
"\n",
" auc = roc_auc_score(y_test, y_prob)\n",
" pr_auc = average_precision_score(y_test, y_prob)\n",
" brier = brier_score_loss(y_test, y_prob)\n",
"\n",
" results_list.append({'fold': fold, 'AUC': auc, 'PR_AUC': pr_auc, 'Brier': brier})\n",
" print(f\"Fold {fold}: AUC={auc:.3f}, PR_AUC={pr_auc:.3f}, Brier={brier:.3f}\")\n",
"\n",
" if plot_curves:\n",
" # ROC curve\n",
" fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
" plt.subplot(1,2,1)\n",
" plt.plot(fpr, tpr, label=f'Fold {fold}')\n",
" plt.xlabel('FPR'); plt.ylabel('TPR'); plt.title('ROC Curves'); plt.legend()\n",
" # Precision-Recall curve\n",
" precision, recall, _ = precision_recall_curve(y_test, y_prob)\n",
" plt.subplot(1,2,2)\n",
" plt.plot(recall, precision, label=f'Fold {fold}')\n",
" plt.xlabel('Recall'); plt.ylabel('Precision'); plt.title('PR Curves'); plt.legend()\n",
"\n",
" results_df = pd.DataFrame(results_list)\n",
" mean_metrics = results_df[['AUC','PR_AUC','Brier']].mean()\n",
"\n",
" if plot_curves:\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" print(\"\\nAverage metrics across folds:\")\n",
" print(mean_metrics)\n",
"\n",
" return results_df, mean_metrics\n"
]
},
{
"cell_type": "markdown",
"id": "33908b30-3def-4e02-b90a-d3de0004766b",
"metadata": {
"id": "33908b30-3def-4e02-b90a-d3de0004766b"
},
"source": [
"## Define Study Area & Sampling Blocks\n",
"### 🌍 Rationale for Selecting Ebolowa, Southern Cameroon as Pilot Area\n",
"\n",
"**Objective:** Choose a pilot region to evaluate whether Google satellite embeddings can capture **fine-scale land-use changes** (plantations, small clearings, roads) while remaining tropical and geographically relatable.\n",
"\n",
"---\n",
"\n",
"### ✅ Advantages of Ebolowa\n",
"\n",
"1. **Recognizable location**\n",
" - Ebolowa is the **capital of Cameroon’s South Region**, making the study area geographically and administratively notable.\n",
" - Anchors the project in a familiar context for stakeholders or readers.\n",
"\n",
"2. **Tropical latitude and climate**\n",
" - Approximately **2.9° N, 11.2° E**, placing the pilot area firmly within the tropical rainforest biome and making it regionally representative of a tropical climate.\n",
" - Maintains comparable forest type and seasonal climate conditions (wet and dry seasons).\n",
"\n",
"3. **High forest heterogeneity**\n",
" - Surrounding area includes dense tropical forest, secondary regrowth, smallholder agriculture, and **plantations (e.g., oil palm)**.\n",
" - Presence of small roads and settlements provides **fine-scale land-use variation** for testing embeddings’ sensitivity.\n",
"\n",
"4. **Manageable study tile**\n",
" - A **100 km × 100 km tile** around Ebolowa supports 5 km × 5 km blocks (400 blocks), enabling a **robust block-based sampling design** while remaining computationally feasible in Earth Engine.\n",
"\n",
"---\n",
"\n",
"### ⚠️ Considerations / Limitations\n",
"\n",
"1. **Commercial plantations**\n",
" - Industrial crops may dominate some areas, potentially influencing JRC forest labels.\n",
" - Mitigation: stratify blocks to maintain a balance of forest and non-forest pixels.\n",
"\n",
"2. **Cloud coverage**\n",
" - Seasonal wet months may limit Sentinel-2 observations.\n",
" - Mitigation: rely on Sentinel-1 features and S2 coverage flags.\n",
"\n",
"---\n",
"\n",
"### ✅ Summary\n",
"\n",
"Ebolowa, Southern Cameroon, is an **ideal pilot area** because it: \n",
"- Combines recognisable geography with tropical forest conditions. \n",
"- Offers **fine-scale human-modified landscape heterogeneity** for robust testing of embeddings. \n",
"- Supports a **100 km × 100 km, 5 km block-based sampling** framework feasible for Earth Engine processing.\n",
"\n",
"> *Recommendation*: Use this area as the primary evaluation site for testing Google satellite embeddings’ ability to detect forest and land-use patterns, while using a location like Buta, DRC, for initial pipeline debugging and cross-validation.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9cb22182-bbf1-469a-8daa-b1e1edbe3e78",
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 235,
"status": "ok",
"timestamp": 1759947947433,
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"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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"user_tz": -120
},
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"outputId": "41ccb9a8-70f6-4abe-f1ca-b40c825f90ec"
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"outputs": [],
"source": [
"# Step 1: Define the central point (Ebolowa)\n",
"lon, lat = 11.1122402, 2.921053 # (longitude, latitude)\n",
"point = Point(lon, lat)\n",
"\n",
"# Step 2: Create a GeoDataFrame in WGS84\n",
"gdf = gpd.GeoDataFrame({'geometry': [point]}, crs='EPSG:4326')\n",
"\n",
"# Step 3: Estimate appropriate UTM CRS automatically\n",
"utm_crs = gdf.estimate_utm_crs(datum_name='WGS 84')\n",
"gdf_utm = gdf.to_crs(utm_crs)\n",
"\n",
"# Step 4: Create a 50 km buffer\n",
"buffer_50km = gdf_utm.buffer(50000)\n",
"gdf_buffer = gpd.GeoDataFrame({'geometry': buffer_50km}, crs=gdf_utm.crs)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "500eaa8b-6ebc-4249-bd81-a783235ab6bb",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 60,
"status": "ok",
"timestamp": 1759947947495,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "500eaa8b-6ebc-4249-bd81-a783235ab6bb",
"outputId": "a327a97c-2b48-4736-b11e-cdb289503c22"
},
"outputs": [],
"source": [
"# Step 1: Get bounds of your 50 km buffer (or your initial point)\n",
"minx, miny, maxx, maxy = gdf_buffer.total_bounds # UTM coordinates in meters\n",
"\n",
"# Step 2: Snap lower-left corner to nearest 5 km (5000 m)\n",
"snap_size = 5000 # 5 km\n",
"lower_left_x = np.floor(minx / snap_size) * snap_size\n",
"lower_left_y = np.floor(miny / snap_size) * snap_size\n",
"\n",
"# Step 3: Define the adjusted 100 km x 100 km study area\n",
"size = 100000 # 100 km\n",
"upper_right_x = lower_left_x + size\n",
"upper_right_y = lower_left_y + size\n",
"\n",
"from shapely.geometry import box\n",
"study_area = box(lower_left_x, lower_left_y, upper_right_x, upper_right_y)\n",
"\n",
"# Step 4: Create GeoDataFrame\n",
"gdf_study_area = gpd.GeoDataFrame({'geometry':[study_area]}, crs=gdf_utm.crs)\n",
"\n",
"# Step 5: Compute centroid\n",
"gdf_study_area['centroid'] = gdf_study_area.centroid\n",
"\n",
"# Optional: Inspect centroid coordinates\n",
"centroid = gdf_study_area['centroid'].iloc[0]\n",
"print(f\"Centroid coordinates (UTM): x={centroid.x}, y={centroid.y}\")\n",
"\n",
"# # Optional: Plot for visualization\n",
"# ax = gdf_study_area.plot(edgecolor='blue', facecolor='none', figsize=(6,6))\n",
"# gdf_study_area['centroid'].plot(ax=ax, color='red', marker='o', markersize=50)\n",
"# gdf_utm.plot(ax=ax, color='grey', marker='o', markersize=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d693ecd-9014-4f1d-ace8-37d3dcc5d6ca",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 493
},
"executionInfo": {
"elapsed": 3949,
"status": "ok",
"timestamp": 1759947951435,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "5d693ecd-9014-4f1d-ace8-37d3dcc5d6ca",
"outputId": "e7254e2a-824d-4e50-a40b-ae339040d559"
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"outputs": [],
"source": [
"# Convert boundary to Earth Engine Geometry\n",
"boundary_gdf = gdf_study_area.copy()\n",
"if boundary_gdf.crs is not None and boundary_gdf.crs.to_epsg() != 4326:\n",
" boundary_gdf = boundary_gdf.to_crs(\"EPSG:4326\")\n",
"boundary_geom = ee.Geometry.Polygon(boundary_gdf.geometry.iloc[0].exterior.coords[:])\n",
"\n",
"# Step 1: create AOI & grids\n",
"buffered_grid_wgs84, clip_extent_GEE, m = create_aoi_map(boundary_gdf, zoom_start=9)\n",
"\n",
"# Show the current project extent\n",
"fig = branca_figure(width=600, height=400)\n",
"\n",
"fig.add_child(m)\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78288fa5-1c2d-4815-8021-f7d258770d5b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"executionInfo": {
"elapsed": 5,
"status": "ok",
"timestamp": 1759947951447,
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"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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"user_tz": -120
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"outputId": "7ecb6247-81ef-4ac2-d13f-306123e91335"
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"outputs": [],
"source": [
"buffered_grid_wgs84.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f6dd710-2fa3-4551-a7d0-5c9c906b5bfb",
"metadata": {
"executionInfo": {
"elapsed": 6,
"status": "ok",
"timestamp": 1759947951455,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "5f6dd710-2fa3-4551-a7d0-5c9c906b5bfb"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "31e3c0ab-24e4-4d7b-a3b2-d83a5784f991",
"metadata": {
"id": "31e3c0ab-24e4-4d7b-a3b2-d83a5784f991"
},
"source": [
"## 🧭 Sampling Pixels Within Each Block\n",
"\n",
"### 🎯 Objective\n",
"For each **5 × 5 km** block, generate a balanced and spatially representative set of **300 sample points** (or as many as available):\n",
"\n",
"- 150 points located **inside forest** (JRC = 1) \n",
"- 150 points located **outside forest** (JRC = 0) \n",
"- If a class has fewer available points than 150, sample as many as possible \n",
"- **Filling missing points from the opposite class is disabled** to respect true data availability \n",
"\n",
"---\n",
"\n",
"<details>\n",
"<summary>🪄 <b>Method Overview</b></summary>\n",
"\n",
"### 1️⃣ Retrieve Block Geometry\n",
"- Input: single 5 × 5 km block polygon (UTM)\n",
"- Convert geometry to Earth Engine `ee.Geometry.Polygon`\n",
"\n",
"---\n",
"\n",
"### 2️⃣ Generate Sampling Grid (100 m spacing)\n",
"- Create a **regular grid** with 100 m × 100 m spacing \n",
"- Produces **50 × 50 = 2,500 candidate points**\n",
"- Ensures ≥100 m spacing, reducing spatial autocorrelation\n",
"\n",
"---\n",
"\n",
"### 3️⃣ Prepare JRC Global Forest Cover Layer\n",
"<!-- ```python\n",
"image2020 = ee.ImageCollection('JRC/GFC2020/V2').mosaic()\n",
" ``` -->\n",
"- Use Map band (1 = forest)\n",
"- Clip to block extent for efficiency\n",
"- Pixel resolution: 10 m\n",
"\n",
"---\n",
"\n",
"### 4️⃣ Sample Grid Points over JRC Layer\n",
"\n",
"- Use the **100 m grid points** created in Step 2 as sampling locations \n",
"- Sample the JRC layer using Earth Engine’s `sampleRegions()` function \n",
"- Sampling is done at **10 m scale**, matching the JRC dataset resolution \n",
"- Only pixels with valid data are returned (i.e., no-data areas are excluded) \n",
"\n",
"**Processing notes:**\n",
"- The result is an `ee.FeatureCollection` where each point has a `forest_class` attribute:\n",
" - `1` → Forest (JRC pixel = 1)\n",
" - `0` → Non-forest (JRC pixel = 0)\n",
"- Since only valid pixels are returned, **non-forest points are explicitly represented** before sampling if needed\n",
"\n",
"---\n",
"\n",
"### 5️⃣ Split and Randomize Samples\n",
"\n",
"- Split the sampled dataset into two subsets:\n",
" - `forest_points` → features with `forest_class = 1`\n",
" - `nonforest_points` → features with `forest_class = 0`\n",
"- Randomly select up to **150 samples** from each subset using a fixed random seed for reproducibility \n",
"- If a subset has fewer than 150 points, **do not fill** from the opposite class \n",
"- This ensures **all available points are used while maintaining class balance as much as possible**\n",
"\n",
"---\n",
"\n",
"### 6️⃣ Merge and Finalize Samples\n",
"\n",
"- Combine both subsets into a single `ee.FeatureCollection` \n",
"- Attach sampling metadata to each feature:\n",
"\n",
"| Attribute | Description |\n",
"|------------|--------------|\n",
"| `block_id` | Unique block identifier (e.g., `\"005-012\"`) |\n",
"| `forest_class` | `1` = forest, `0` = non-forest |\n",
"| `x`, `y` | UTM coordinates of the sample point |\n",
"| `n_in_actual` | Number of true forest pixels found in this block |\n",
"| `filled_flag` | `True` if one class was undersampled and filled from the other (currently always `False`) |\n",
"\n",
"- Validate that the total count is equal to the number of points actually sampled (≤ 300)\n",
"\n",
"---\n",
"\n",
"### 7️⃣ Output\n",
"\n",
"- Export one `FeatureCollection` per block \n",
"- Each dataset represents **balanced samples of forest and non-forest pixels according to availability**\n",
"\n",
"**Export options:**\n",
"- To Earth Engine Asset for direct cloud-based processing \n",
"- To GeoJSON or shapefile for local analysis and model training \n",
"\n",
"**Example filenames:**\n",
"- `samples_all_blocks.geojson`\n",
"\n",
"---\n",
"\n",
"### 🧩 Notes and Rationale\n",
"\n",
"- Sampling 2,500 candidate points (100 m grid) is efficient — about **1% of all 10 m pixels** per block \n",
"- The **100 m spacing** enforces a minimum distance between samples, reducing spatial autocorrelation \n",
"- Keeping a **target of 300 points per block** simplifies downstream analysis while respecting data availability \n",
"- Including blocks with few forest or non-forest pixels retains important ecological gradients (e.g., forest edge regions) \n",
"- Random sampling ensures representative and unbiased coverage within each block "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ddc81b97-cd3e-47a9-9f8e-11b0267b23c6",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 52,
"status": "ok",
"timestamp": 1759947951510,
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"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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"user_tz": -120
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"outputs": [],
"source": [
"# --- Set overwrite flag ---\n",
"boo_run = False\n",
"overwrite = False # True # Change to False to skip if file exists\n",
"\n",
"# --- Define output filename ---\n",
"output_file = \"all_block_samples.geojson\"\n",
"\n",
"# --- Check if file exists ---\n",
"if os.path.exists(output_file) and not overwrite:\n",
" print(f\"File '{output_file}' already exists. Skipping this block.\")\n",
" boo_run = False\n",
"else:\n",
" # --- Your sampling code here ---\n",
" # e.g., creating all_samples_gdf\n",
" # all_samples_gdf = ...\n",
" boo_run = True\n",
"\n",
" # Save to GeoJSON\n",
" # all_samples_gdf.to_file(output_file, driver=\"GeoJSON\")\n",
" # print(f\"Saved all_samples_gdf as '{output_file}' (overwrite={overwrite}).\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5bc532bc-7be5-4590-8d0b-6b6d00b5a91c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 9,
"status": "ok",
"timestamp": 1759947951522,
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"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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"user_tz": -120
},
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"outputId": "e558c55c-4386-48f7-d061-8dc1158752fb"
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"outputs": [],
"source": [
"if boo_run:\n",
" # --- 0. Load JRC image (forest = 1, non-forest = 0) ---\n",
" image2020 = ee.ImageCollection('JRC/GFC2020/V2').mosaic().select('Map').unmask(0)\n",
"\n",
" print(\"JRC bands:\", image2020.bandNames().getInfo())\n",
"\n",
" block_stats = []\n",
"\n",
" # Loop over all blocks with a progress bar\n",
" for i, row in tqdm(buffered_grid_wgs84.iterrows(), total=len(buffered_grid_wgs84), desc=\"Processing blocks\"):\n",
" block_id = row['block_id']\n",
"\n",
" # Convert geometry to EE\n",
" block_geom = ee.Geometry.Polygon(list(row.geometry.exterior.coords))\n",
"\n",
" # Clip JRC image to block\n",
" jrc_block = image2020.clip(block_geom).unmask(0).rename('Forest')\n",
"\n",
" # Reduce the image to counts per class at coarser scale\n",
" counts_dict = jrc_block.reduceRegion(\n",
" reducer=ee.Reducer.frequencyHistogram(),\n",
" geometry=block_geom,\n",
" scale=100, # coarser scale → much faster\n",
" maxPixels=1e9\n",
" ).get('Forest').getInfo()\n",
"\n",
" n_forest = int(round(counts_dict.get('1', 0)))\n",
" n_nonforest = int(round(counts_dict.get('0', 0)))\n",
"\n",
" # Store results\n",
" block_stats.append({\n",
" 'block_id': block_id,\n",
" 'n_forest': n_forest,\n",
" 'n_nonforest': n_nonforest\n",
" })\n",
"\n",
" # Convert to DataFrame\n",
" stats_df = pd.DataFrame(block_stats)\n",
"\n",
" # Filter blocks if needed\n",
" filtered_blocks = stats_df[(stats_df['n_forest'] >= 1) & (stats_df['n_nonforest'] >= 1)]\n",
"\n",
" print(\"Blocks passing threshold:\", len(filtered_blocks))\n",
" filtered_blocks.head()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1d93d9b5-c827-45a7-bff0-4925804044d0",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 9,
"status": "ok",
"timestamp": 1759947951534,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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"user_tz": -120
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"source": [
"if boo_run:\n",
" # Select blocks with ≥25 non-forest pixels\n",
" nonforest_blocks_threshold = stats_df[stats_df['n_nonforest'] >= 25]\n",
" count_nonforest_blocks = len(nonforest_blocks_threshold)\n",
" sum_nonforest_pixels = nonforest_blocks_threshold['n_nonforest'].sum()\n",
"\n",
" print(f\"Number of blocks with ≥25 non-forest pixels: {count_nonforest_blocks}\")\n",
" print(f\"Total non-forest pixels in these blocks: {sum_nonforest_pixels}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b1952d1-5787-4ed4-8580-89a5f71c8b75",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 8,
"status": "ok",
"timestamp": 1759947951545,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "0b1952d1-5787-4ed4-8580-89a5f71c8b75",
"outputId": "e7e40662-1b12-43bc-ccf2-5b7c3f76d05c"
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"outputs": [],
"source": [
"if boo_run:\n",
" #\n",
"\n",
" plt.figure(figsize=(10,6))\n",
" sns.histplot(stats_df, x='n_forest', binwidth=25, color='green', label='Forest', alpha=0.6)\n",
" sns.histplot(stats_df, x='n_nonforest', bins=30, color='brown', label='Non-Forest', alpha=0.6)\n",
" plt.xlabel(\"Number of pixels\")\n",
" plt.ylabel(\"Number of blocks\")\n",
" plt.title(\"Distribution of Forest and Non-Forest Pixels per Block\")\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8809df56-aed3-4006-8fdc-f55c51b17616",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 9,
"status": "ok",
"timestamp": 1759947951557,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "8809df56-aed3-4006-8fdc-f55c51b17616",
"outputId": "d7494c0a-1cf9-41e3-99ff-9c92ccdcf9d8"
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"outputs": [],
"source": [
"if boo_run:\n",
" # Subset blocks with ≥25 non-forest pixels\n",
" blocks_nf25 = stats_df[stats_df['n_nonforest'] >= 25]\n",
"\n",
" # Inspect basic stats\n",
" print(blocks_nf25[['block_id', 'n_forest', 'n_nonforest']].describe())\n",
"\n",
" # Plot distribution of non-forest pixels\n",
" plt.figure(figsize=(8,6))\n",
" sns.histplot(blocks_nf25['n_nonforest'], bins=15, color='brown')\n",
" plt.xlabel(\"Number of non-forest pixels\")\n",
" plt.ylabel(\"Number of blocks\")\n",
" plt.title(\"Distribution of non-forest pixels in blocks with ≥25 non-forest\")\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"id": "56c59ac2-5ade-4f29-995c-efb052ab2575",
"metadata": {
"id": "56c59ac2-5ade-4f29-995c-efb052ab2575"
},
"source": [
"We have now 46 blocks to work with, and select the samples from these 46 blocks.\n",
"\n",
"Update on the method, for each block we sample up to 150 points random"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f17820f-b9fb-498a-8800-63be9d53fe47",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 10,
"status": "ok",
"timestamp": 1759947951571,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "3f17820f-b9fb-498a-8800-63be9d53fe47",
"outputId": "483126d8-f7b8-4bf9-cdf7-5542b3509baf"
},
"outputs": [],
"source": [
"if boo_run:\n",
" #\n",
" blocks_nf25.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f1ce23e-ba12-4413-8479-2b15de531d4b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 9,
"status": "ok",
"timestamp": 1759947951584,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "7f1ce23e-ba12-4413-8479-2b15de531d4b",
"outputId": "06526d84-b4b6-4041-80ac-5248520779b1"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # Select only the blocks with ≥25 non-forest pixels\n",
" blocks_to_sample = blocks_nf25.copy() #.head() # or all if you want all 46\n",
"\n",
" # Merge with grid geometries\n",
" blocks_gdf = blocks_to_sample.merge(\n",
" buffered_grid_wgs84[['block_id', 'geometry']],\n",
" on='block_id',\n",
" how='left'\n",
" )\n",
"\n",
" # Ensure it's a GeoDataFrame\n",
" blocks_gdf = gpd.GeoDataFrame(blocks_gdf, geometry='geometry', crs=buffered_grid_wgs84.crs)\n",
"\n",
" blocks_gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f567ea2-5475-481b-aad7-917d034e0cca",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 9,
"status": "ok",
"timestamp": 1759947951596,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "4f567ea2-5475-481b-aad7-917d034e0cca",
"outputId": "d6bc777a-7e2b-4315-f7de-9cab1353d7e0"
},
"outputs": [],
"source": [
"#boo_run = False\n",
"blocks_file=\"selected_block.geojson\"\n",
"if boo_run:\n",
" # Save all samples as GeoJSON\n",
" blocks_gdf.to_file(blocks_file, driver=\"GeoJSON\")\n",
"\n",
" print(\"Saved blocks_gdf as 'selected_block.geojson' in the project root.\")\n",
"else:\n",
" blocks_gdf = gpd.read_file(blocks_file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab963607-4fd2-4ef8-bed2-3da93109f485",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 49,
"status": "ok",
"timestamp": 1759947951647,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "ab963607-4fd2-4ef8-bed2-3da93109f485",
"outputId": "55b93000-1027-4edf-aa1f-cdbf32c0a38d"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # def sample_jrc_on_grid(JRC_forest, block_geom, block_id, spacing=100, sample_scale=10):\n",
" # def balance_samples(samples_gdf, n_per_class=250, block_id=None, fill=True):\n",
"\n",
" # Select first block\n",
" block_geom_wgs = blocks_gdf.geometry.iloc[0]\n",
" gee_block_geom = ee.Geometry.Polygon(list(block_geom_wgs.exterior.coords))\n",
"\n",
" block_id = blocks_gdf.block_id.iloc[0]\n",
"\n",
" # Sample JRC points on the block\n",
" sample_fc, jrc_block = sample_jrc_on_grid(image2020, gee_block_geom, block_id, spacing=100, sample_scale=10)\n",
"\n",
" # Convert to GeoDataFrame\n",
" sample_gdf = geemap.ee_to_gdf(sample_fc)\n",
"\n",
" # Balance samples: 150 points per class, no filling\n",
" field_name = \"Forest\"\n",
" balanced_gdf = balance_samples(sample_gdf, field_name, n_per_class=150, block_id=block_id, fill=False)\n",
"\n",
" # Inspect\n",
" print(balanced_gdf.head())\n",
" print(balanced_gdf[field_name].value_counts())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b4e5bf7-b5c8-41bf-bb90-07326ddcad03",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 7,
"status": "ok",
"timestamp": 1759947951656,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "8b4e5bf7-b5c8-41bf-bb90-07326ddcad03",
"outputId": "aa6188ad-f007-4c2b-8ea3-7e94b5c50716"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # # Create a folium map\n",
" # center = gee_block_geom.centroid().coordinates().getInfo()[::-1] # lat/lon\n",
" # m = geemap.Map(location=center, zoom_start=12)\n",
"\n",
" # # Visualization parameters\n",
" # vis_params = {\n",
" # 'min': 0,\n",
" # 'max': 1,\n",
" # 'palette': ['lightgray', 'green'] # 0 = non-forest, 1 = forest\n",
" # }\n",
"\n",
" # # Add layer\n",
" # m.add_ee_layer(jrc_block, vis_params, 'JRC Forest Block')\n",
"\n",
" # # Display map in notebook\n",
" # m\n",
" print(\"map\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbe4c485-32ae-45b7-9670-602ace12b2e8",
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1759947951662,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "cbe4c485-32ae-45b7-9670-602ace12b2e8"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "4acf3f81-546b-4685-ab72-ef0190ccab35",
"metadata": {
"id": "4acf3f81-546b-4685-ab72-ef0190ccab35"
},
"source": [
"Running the loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57380ae2-8395-4f57-aed5-41649e4c700a",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 7,
"status": "ok",
"timestamp": 1759947951674,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "57380ae2-8395-4f57-aed5-41649e4c700a",
"outputId": "4df28de1-dcb9-4b5c-d328-39704f323c9e"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # Assume:\n",
" # - JRC_forest is the ee.Image (10 m resolution, 0/1 forest)\n",
" # - blocks_nf25 is a GeoDataFrame with the 46 “good” blocks\n",
" # - sample_jrc_on_grid() and balance_samples() are defined\n",
" # - n_forest_points = 150, n_nonforest_points = 50\n",
"\n",
" all_samples_list = []\n",
"\n",
" # Loop with progress bar\n",
" for idx in tqdm(range(len(blocks_gdf)), desc=\"Sampling blocks\"):\n",
" block_row = blocks_gdf.iloc[idx]\n",
" block_geom = block_row.geometry\n",
" block_id = block_row.block_id\n",
"\n",
" # Convert geometry to EE\n",
" gee_block_geom = ee.Geometry.Polygon(list(block_geom.exterior.coords))\n",
"\n",
" # Sample points in the block\n",
" sample_fc, jrc_block = sample_jrc_on_grid(\n",
" JRC_forest=image2020,\n",
" block_geom=gee_block_geom,\n",
" block_id=block_id,\n",
" spacing=100, # grid spacing for sampling (m)\n",
" sample_scale=10 # GEE sampling scale (m)\n",
" )\n",
"\n",
" # Convert to GeoDataFrame\n",
" sample_gdf = geemap.ee_to_gdf(sample_fc)\n",
"\n",
" # Balance samples: forest=150, non-forest=50, no fill\n",
" field_name = \"Forest\"\n",
" balanced_gdf = balance_samples(\n",
" samples_gdf=sample_gdf,\n",
" field_name=field_name,\n",
" n_per_class=150,\n",
" block_id=block_id,\n",
" fill=False\n",
" )\n",
"\n",
" all_samples_list.append(balanced_gdf)\n",
"\n",
" # Concatenate all samples into one GeoDataFrame\n",
" all_samples_gdf = pd.concat(all_samples_list, ignore_index=True)\n",
"\n",
" print(f\"Total number of sampled points: {len(all_samples_gdf)}\")\n",
" all_samples_gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39fc60f0-0a20-4649-8cc4-8386eb2f7166",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 16,
"status": "ok",
"timestamp": 1759947951693,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "39fc60f0-0a20-4649-8cc4-8386eb2f7166",
"outputId": "d0ea75c9-30cf-43f3-aa1f-a63e03d89b3c"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # Save all samples as GeoJSON\n",
" all_samples_gdf.to_file(\"all_block_samples.geojson\", driver=\"GeoJSON\")\n",
"\n",
" print(\"Saved all_samples_gdf as 'all_samples.geojson' in the project root.\")"
]
},
{
"cell_type": "markdown",
"id": "e509e0b3-08e4-4a7f-a1c9-68efd90da110",
"metadata": {
"id": "e509e0b3-08e4-4a7f-a1c9-68efd90da110"
},
"source": [
"### 8️⃣Test Plotting"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d841f49-0085-46d0-8eab-013f97807dc3",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 18,
"status": "ok",
"timestamp": 1759947951702,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "9d841f49-0085-46d0-8eab-013f97807dc3",
"outputId": "e5451edf-3608-4349-ee5d-0ba4fd86daae"
},
"outputs": [],
"source": [
"#all_samples_gdf.plot()\n",
"#boo_run = True"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82b821b9-c6c6-41e7-a9cc-387c78050370",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 11,
"status": "ok",
"timestamp": 1759947951706,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "82b821b9-c6c6-41e7-a9cc-387c78050370",
"outputId": "8a2fffa5-1311-4613-bea6-8578969d0e6d"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # # --- 1. Get map center from the samples ---\n",
" # center = [all_samples_gdf.geometry.y.mean(), all_samples_gdf.geometry.x.mean()]\n",
"\n",
" # # --- 2. Create Folium map ---\n",
" # m2 = folium.Map(location=center, zoom_start=12, tiles=\"CartoDB Positron\")\n",
"\n",
" # # --- 3. Function to style points ---\n",
" # def style_function(feature):\n",
" # color = 'green' if feature['properties']['Forest'] == 1 else 'blue'\n",
" # return {\n",
" # 'radius': 3,\n",
" # 'color': color,\n",
" # 'fillColor': color,\n",
" # 'fillOpacity': 0.7,\n",
" # }\n",
"\n",
" # # --- 4. Add points as CircleMarkers ---\n",
" # for idx, row in all_samples_gdf.iterrows():\n",
" # folium.CircleMarker(\n",
" # location=[row.geometry.y, row.geometry.x],\n",
" # radius=3,\n",
" # color='green' if row['Forest']==1 else 'blue',\n",
" # fill=True,\n",
" # fill_color='green' if row['Forest']==1 else 'blue',\n",
" # fill_opacity=0.7,\n",
" # ).add_to(m2)\n",
"\n",
" # # --- 5. Show map ---\n",
" # # Show the current project extent\n",
" # fig2 = branca_figure(width=600, height=400)\n",
"\n",
" # fig2.add_child(m2)\n",
" # fig2\n",
" print(\"Map\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bab4883c-f924-4d5c-bc9b-e931eb99442c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 8,
"status": "ok",
"timestamp": 1759947951715,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "bab4883c-f924-4d5c-bc9b-e931eb99442c",
"outputId": "57298d67-6391-44ee-d422-a1e428a5a264"
},
"outputs": [],
"source": [
"if boo_run:\n",
" ##cmap = plt.colormaps['Greens'].resampled(2)\n",
" cmap = ListedColormap(['lightgray', 'green']) # 0=non-forest, 1=forest\n",
"\n",
" # Convert geometry to EE\n",
" gee_block_geom = ee.Geometry.Polygon(list(block_geom.exterior.coords))\n",
"\n",
" # Ensure jrc_block has a valid projection\n",
" proj = image2020.projection().atScale(10)\n",
"\n",
" # Clip and unmask directly\n",
" jrc_block_fixed = image2020.clip(gee_block_geom).unmask(0).rename('Forest')\n",
"\n",
" jrc_block_fixed = jrc_block_fixed.set({'system:time_start': ee.Date('2020-12-31T23:59:00').millis()})\n",
"\n",
" # Open in xarray\n",
" ds_block = xr.open_dataset(\n",
" jrc_block_fixed,\n",
" engine='ee',\n",
" geometry=gee_block_geom,\n",
" scale=10,\n",
" projection=proj\n",
" )\n",
"\n",
" # Select the Forest band\n",
" da = ds_block['Forest']\n",
"\n",
" # Convert to numpy array and remove the extra dimension\n",
" arr = da.values.squeeze() # removes singleton dimensions, shape becomes (height, width)\n",
"\n",
" # Flip vertically (north up) and horizontally (west left) as needed\n",
" arr = (np.fliplr(arr)) # flips both axes\n",
"\n",
" # Optional: transpose if you want x,y axes to match array columns/rows\n",
" arr = arr.T # only if needed\n",
"\n",
" # Check shape\n",
" print(arr.shape)\n",
"\n",
" # # Plot\n",
" # plt.figure(figsize=(6,6))\n",
" # # cmap = plt.cm.get_cmap('Greens', 2) # 2 discrete colors: 0=non-forest, 1=forest\n",
" # # cmap = ListedColormap(['lightgray', 'green']) # 0=non-forest, 1=forest\n",
" # plt.imshow(arr, cmap=cmap, origin='upper')\n",
" # plt.colorbar(ticks=[0,1], label='Forest class (0=non-forest, 1=forest)')\n",
" # plt.title('JRC Forest / Non-Forest')\n",
" # plt.axis('off')\n",
" # plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "245ec991-bc35-4275-a3a5-6a4d3768f459",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 13,
"status": "ok",
"timestamp": 1759947951731,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "245ec991-bc35-4275-a3a5-6a4d3768f459",
"outputId": "637319ec-68c2-411e-8195-55671acb4b48"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # Assume arr is already 2D, correctly oriented (rows=y, cols=x)\n",
" height, width = arr.shape\n",
"\n",
" # Get coordinates\n",
" xres = ds_block['lon'][1] - ds_block['lon'][0]\n",
" yres = ds_block['lat'][1] - ds_block['lat'][0]\n",
"\n",
" transform = from_origin(\n",
" ds_block['lon'].min().item(), # top-left x\n",
" ds_block['lat'].max().item(), # top-left y\n",
" xres.item(),\n",
" yres.item()\n",
" )\n",
"\n",
" # Use MemoryFile to avoid writing to disk\n",
" from rasterio.io import MemoryFile\n",
"\n",
" with MemoryFile() as memfile:\n",
" with memfile.open(\n",
" driver='GTiff',\n",
" height=height,\n",
" width=width,\n",
" count=1,\n",
" dtype=arr.dtype,\n",
" crs='EPSG:3857',\n",
" transform=transform\n",
" ) as dataset:\n",
" dataset.write(arr, 1) # write first band\n",
"\n",
" # Now vectorize\n",
" mask=None\n",
" #mask = arr != 0 # only forest pixels\n",
"\n",
" polygons = []\n",
" values = []\n",
"\n",
" # Replace NaN or invalid values with 0 (or another suitable class)\n",
" arr_clean = np.nan_to_num(arr, nan=0).astype('uint8')\n",
"\n",
" for geom, value in shapes(arr_clean, mask=None, transform=transform):\n",
" polygons.append(shape(geom))\n",
" values.append(value)\n",
"\n",
" gdf = gpd.GeoDataFrame({'forest': values}, geometry=polygons, crs='EPSG:3857')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef06823b-f3e0-42de-a36e-7aea8ce2bba9",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 12,
"status": "ok",
"timestamp": 1759947951738,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "ef06823b-f3e0-42de-a36e-7aea8ce2bba9",
"outputId": "df77a115-7e99-4b1d-8098-a8334552069c"
},
"outputs": [],
"source": [
"if boo_run:\n",
" #print(gdf.head(5))\n",
" #gdf.plot()\n",
"\n",
" forest_gdf = gdf.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2626a1a-1222-4b9c-8af9-e51eb16632c3",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 51,
"status": "ok",
"timestamp": 1759947951790,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "a2626a1a-1222-4b9c-8af9-e51eb16632c3",
"outputId": "2cdf12aa-995e-4fdc-cf28-b326e14c7864"
},
"outputs": [],
"source": [
"if boo_run:\n",
" # --- 1. Filter data for the selected block ---\n",
" block_id = '020-020'\n",
" #block_id = '003-009'\n",
"\n",
" block_poly = blocks_gdf[blocks_gdf['block_id'] == block_id]\n",
"\n",
" # Filter polygons by forest value\n",
" forest_subset = forest_gdf[\n",
" (forest_gdf['forest'] == 1) &\n",
" (forest_gdf.intersects(block_poly.geometry.union_all()))\n",
" ]\n",
"\n",
" non_forest_subset = forest_gdf[\n",
" (forest_gdf['forest'] == 0) &\n",
" (forest_gdf.intersects(block_poly.geometry.union_all()))\n",
" ]\n",
"\n",
" samples_subset = all_samples_gdf[all_samples_gdf['block_id'] == block_id]\n",
"\n",
" # --- 2. Create figure and axes ---\n",
" fig, ax = plt.subplots(figsize=(8, 8))\n",
"\n",
" # --- 3. Plot polygons ---\n",
" block_poly.boundary.plot(ax=ax, color='black', linewidth=2)\n",
" forest_subset.plot(ax=ax, color='green', alpha=0.25, edgecolor='none')\n",
" non_forest_subset.plot(ax=ax, color='blue', alpha=0.25, edgecolor='none')\n",
"\n",
" # --- 4. Overlay points ---\n",
" samples_subset[samples_subset['Forest'] == 1].plot(ax=ax, color='green', markersize=15)\n",
" samples_subset[samples_subset['Forest'] == 0].plot(ax=ax, color='blue', markersize=15)\n",
"\n",
" # --- 5. Style plot ---\n",
" ax.set_title(f\"Block {block_id}: Forest vs Non-Forest Samples\", fontsize=14)\n",
" ax.set_xlabel(\"Longitude\")\n",
" ax.set_ylabel(\"Latitude\")\n",
" ax.set_aspect('equal')\n",
"\n",
" # --- 6. Custom legend ---\n",
" forest_patch = mpatches.Patch(color='green', alpha=0.5, label='Forest polygons')\n",
" forest_marker = mlines.Line2D([], [], color='green', marker='o', linestyle='None', markersize=8, label='Forest samples')\n",
" nonforest_marker = mlines.Line2D([], [], color='blue', marker='o', linestyle='None', markersize=8, label='Non-forest samples')\n",
" block_line = mlines.Line2D([], [], color='black', linewidth=2, label='Block boundary')\n",
"\n",
" ax.legend(handles=[block_line, forest_patch, forest_marker, nonforest_marker],\n",
" loc='upper right')\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "69cb397f-694f-4ca9-bbc4-088b674fdb68",
"metadata": {
"id": "69cb397f-694f-4ca9-bbc4-088b674fdb68"
},
"source": [
"### 9️⃣Loading the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62a4e5af-e12b-4a9e-a532-61e6f4c31f0b",
"metadata": {
"executionInfo": {
"elapsed": 11,
"status": "ok",
"timestamp": 1759947951803,
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"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
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},
"id": "62a4e5af-e12b-4a9e-a532-61e6f4c31f0b"
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f9f0140-ab1b-4ca9-a38e-bc742a8973e4",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 333,
"status": "ok",
"timestamp": 1759947952128,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "5f9f0140-ab1b-4ca9-a38e-bc742a8973e4",
"outputId": "35dcb2b7-11ad-4894-878b-b2b6fe35c2e1"
},
"outputs": [],
"source": [
"# Load all_samples_gdf\n",
"boo_run = False\n",
"if boo_run:\n",
" print(\"Sampling already run in this session. Dataframe is available as all_samples_gdf.\")\n",
"else:\n",
" if os.path.exists(output_file):\n",
" print(f\"Loading existing samples from '{output_file}'...\")\n",
" all_samples_gdf = gpd.read_file(output_file)\n",
" blocks_gdf = gpd.read_file(blocks_file)\n",
"\n",
" boo_run = True # now dataframe is available\n",
" else:\n",
" raise FileNotFoundError(f\"No sampling results found at '{output_file}'. Please run the sampling block first.\")\n",
"\n",
"# Count samples per class\n",
"class_counts = all_samples_gdf['Forest'].value_counts().rename({0: 'Non-Forest', 1: 'Forest'})\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c0bf80a5-4b65-4906-86f1-68fe515bd7ac",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 608
},
"executionInfo": {
"elapsed": 533,
"status": "ok",
"timestamp": 1759947952663,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "c0bf80a5-4b65-4906-86f1-68fe515bd7ac",
"outputId": "b5ca82a2-9d60-4fa7-d477-395773847acc"
},
"outputs": [],
"source": [
"# Count samples per block and class\n",
"block_class_counts = (\n",
" all_samples_gdf.groupby(['block_id', 'Forest'])\n",
" .size()\n",
" .unstack(fill_value=0)\n",
" .rename(columns={0: 'Non-Forest', 1: 'Forest'})\n",
")\n",
"\n",
"# Identify blocks meeting the full 150 samples per class\n",
"full_blocks = block_class_counts[\n",
" (block_class_counts['Forest'] >= 150) &\n",
" (block_class_counts['Non-Forest'] >= 150)\n",
"]\n",
"\n",
"n_full_blocks = len(full_blocks)\n",
"n_total_blocks = len(block_class_counts)\n",
"\n",
"print(f\"{n_full_blocks} out of {n_total_blocks} blocks meet the full 150 samples per class.\")\n",
"\n",
"# Filter blocks that are partially filled\n",
"partial_blocks = block_class_counts[\n",
" (block_class_counts['Forest'] < 150) |\n",
" (block_class_counts['Non-Forest'] < 150)\n",
"]\n",
"\n",
"# Plot partially filled blocks\n",
"if not partial_blocks.empty:\n",
" ax = partial_blocks.plot(\n",
" kind='bar',\n",
" figsize=(10, 6),\n",
" color=['blue', 'green'],\n",
" edgecolor='black',\n",
" alpha=0.7\n",
" )\n",
" ax.set_title('Blocks Not Meeting 150 Samples per Class', fontsize=14)\n",
" ax.set_xlabel('Block ID')\n",
" ax.set_ylabel('Number of Samples')\n",
" ax.legend(title='Class')\n",
" ax.grid(axis='y', linestyle='--', alpha=0.6)\n",
" plt.xticks(rotation=45, ha='right')\n",
" plt.tight_layout()\n",
" plt.show()\n",
"else:\n",
" print(\"All blocks have full 150 samples per class.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "007a4006-0543-40ef-8614-b815a05f7adc",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331
},
"executionInfo": {
"elapsed": 69,
"status": "ok",
"timestamp": 1759947952737,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "007a4006-0543-40ef-8614-b815a05f7adc",
"outputId": "de66f690-a71b-4a80-aa35-6cf3d92a32d7"
},
"outputs": [],
"source": [
"partial_blocks\n",
"# Reuse partial_blocks from previous step\n",
"balance_per_block = partial_blocks.copy()\n",
"\n",
"# Display the first few rows\n",
"display(balance_per_block)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c6fd941-6c17-4187-b0a0-1babe188bc24",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 785
},
"executionInfo": {
"elapsed": 4570,
"status": "ok",
"timestamp": 1759947957310,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "3c6fd941-6c17-4187-b0a0-1babe188bc24",
"outputId": "0cffc725-9707-42dd-e099-4d9bda0dccd0"
},
"outputs": [],
"source": [
"# Create figure and axes\n",
"fig, ax = plt.subplots(figsize=(8, 8))\n",
"\n",
"# Plot forest and non-forest points\n",
"all_samples_gdf[all_samples_gdf['Forest'] == 1].plot(\n",
" ax=ax, color='green', markersize=4, label='Forest'\n",
")\n",
"all_samples_gdf[all_samples_gdf['Forest'] == 0].plot(\n",
" ax=ax, color='blue', markersize=2, label='Non-Forest'\n",
")\n",
"\n",
"# Overlay red outlines for partially filled blocks\n",
"partial_blocks_geometry = blocks_gdf[blocks_gdf['block_id'].isin(partial_blocks.index)]\n",
"partial_blocks_geometry.boundary.plot(\n",
" ax=ax, color='red', linewidth=2, label='Partially Filled Blocks'\n",
")\n",
"\n",
"# Add legend\n",
"forest_patch = mpatches.Patch(color='green', label='Forest samples')\n",
"nonforest_patch = mpatches.Patch(color='blue', label='Non-Forest samples')\n",
"partial_patch = mpatches.Patch(color='red', label='Partially Filled Blocks', fill=False, linewidth=2)\n",
"\n",
"ax.legend(handles=[forest_patch, nonforest_patch, partial_patch])\n",
"ax.set_title('Forest vs Non-Forest Samples with Partially Filled Blocks Highlighted')\n",
"ax.set_aspect('equal')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "0707cb49-4a6b-4a31-8339-044e0e95d92f",
"metadata": {
"id": "0707cb49-4a6b-4a31-8339-044e0e95d92f"
},
"source": [
"### 🧩 Summary of Post-Sampling Steps\n",
"\n",
"After generating the balanced per-block samples, the following steps were implemented to manage, verify, and visualize the results:\n",
"\n",
"---\n",
"\n",
"1️⃣ Save All Samples\n",
"- Combined all per-block samples into a single GeoDataFrame `all_samples_gdf`.\n",
"- Exported as **`all_block_samples.geojson`** to the project root.\n",
"- Implemented an **overwrite flag** (`overwrite=True/False`) to prevent accidental reruns.\n",
"\n",
"---\n",
"\n",
"2️⃣ Load or Reuse Results\n",
"- At the start of the workflow, check if the output file already exists.\n",
"- If found and `overwrite=False`, skip the sampling block.\n",
"- Added logic to either **reuse `all_samples_gdf` from memory** or **load it from disk** as needed.\n",
"\n",
"---\n",
"\n",
"3️⃣ Sampling Quality Check\n",
"- Counted the number of forest vs. non-forest samples per block.\n",
"- Identified **blocks not reaching the 150-sample target per class**.\n",
"- Reported how many blocks met the full target and how many were partially filled.\n",
"\n",
"---\n",
"\n",
"4️⃣ Visualization of Partial Blocks\n",
"- Created a **bar chart** showing only the partially filled blocks and their class counts.\n",
"- Added a **map plot** showing all sample points:\n",
" - Forest samples → green points \n",
" - Non-forest samples → blue points \n",
" - Partially filled blocks → red polygon outlines \n",
"\n",
"---\n",
"\n",
"5️⃣ Data Balance Overview\n",
"- Displayed the per-block balance table (`balance_per_block`) for the partially filled blocks.\n",
"- Provided quick sorting options to identify blocks with the fewest valid samples.\n",
"\n",
"---\n",
"\n",
"✅ **Outcome:** \n",
"You now have a complete sampling, validation, and visualization pipeline that: \n",
"- Generates balanced forest/non-forest samples per block, \n",
"- Stores and reloads results safely, \n",
"- Highlights any sampling gaps, and \n",
"- Visually confirms the spatial distribution of all sampled points."
]
},
{
"cell_type": "markdown",
"id": "dbcd8676-a5f9-46ed-88d2-d1d6f01aa0cc",
"metadata": {
"id": "dbcd8676-a5f9-46ed-88d2-d1d6f01aa0cc"
},
"source": [
"## 🌍 Enriching Sample Points with Satellite-Derived Features"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "100667ce-afc1-4b74-8c8d-913f01ed0c1b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 289
},
"executionInfo": {
"elapsed": 1644,
"status": "ok",
"timestamp": 1759947958959,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "100667ce-afc1-4b74-8c8d-913f01ed0c1b",
"outputId": "4d5d2ce5-621d-40e0-c5e5-9d5304e60575"
},
"outputs": [],
"source": [
"# Define your expected output files\n",
"files = {\n",
" \"GE\": \"GE_samples_2020.csv\",\n",
" \"S1\": \"S1_samples_2020.csv\",\n",
" \"S2\": \"S2_samples_2020.csv\"\n",
"}\n",
"\n",
"# Master flag to allow overwriting\n",
"overwrite = False\n",
"\n",
"# Initialize run flags\n",
"boo_RunGE = False\n",
"boo_RunS1 = False\n",
"boo_RunS2 = False\n",
"\n",
"def should_run(dataset_key):\n",
" \"\"\"Check if a dataset should be (re)computed based on file presence.\"\"\"\n",
" file_path = files[dataset_key]\n",
" exists = os.path.exists(file_path)\n",
"\n",
" if exists and not overwrite:\n",
" print(f\"✅ {file_path} already exists — skipping.\")\n",
" return False\n",
" elif exists and overwrite:\n",
" print(f\"⚠️ {file_path} exists but overwrite=True — recomputing.\")\n",
" return True\n",
" else:\n",
" print(f\"🆕 {file_path} missing — computing now.\")\n",
" return True\n",
"\n",
"# Evaluate which parts to run\n",
"boo_RunGE = should_run(\"GE\")\n",
"boo_RunS1 = should_run(\"S1\")\n",
"boo_RunS2 = should_run(\"S2\")\n",
"\n",
"print(\"\\nRun plan:\")\n",
"print(f\" 🛰️ Google Embeddings: {boo_RunGE}\")\n",
"print(f\" 📡 Sentinel-1: {boo_RunS1}\")\n",
"print(f\" 🌈 Sentinel-2: {boo_RunS2}\")\n",
"\n",
"# ======================================================\n",
"# 📍 Prepare Sample Points and Unique IDs\n",
"# ======================================================\n",
"\n",
"# Load once — used by all datasets\n",
"all_samples_gdf = gpd.read_file(\"all_block_samples.geojson\")\n",
"\n",
"samples_with_id_gdf = all_samples_gdf.copy()\n",
"samples_with_id_gdf = samples_with_id_gdf.sort_values(by=[\"block_id\"]).reset_index(drop=True)\n",
"samples_with_id_gdf[\"local_index\"] = samples_with_id_gdf.groupby(\"block_id\").cumcount() + 1\n",
"samples_with_id_gdf[\"local_index_str\"] = samples_with_id_gdf[\"local_index\"].astype(str).str.zfill(4)\n",
"samples_with_id_gdf[\"sample_id\"] = (\n",
" samples_with_id_gdf[\"block_id\"].astype(str) + \"_\" + samples_with_id_gdf[\"local_index_str\"]\n",
")\n",
"\n",
"print(f\"✅ Loaded {len(samples_with_id_gdf)} samples with unique IDs.\")\n",
"print(samples_with_id_gdf[[\"block_id\", \"sample_id\"]].head(5))\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "a2327fc7-59f8-432b-95c9-97fadf63982f",
"metadata": {
"id": "a2327fc7-59f8-432b-95c9-97fadf63982f"
},
"source": [
"### 🌐 Google Embeddings\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdf01306-90e5-4c94-a8b2-e0aba3397f55",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 55
},
"executionInfo": {
"elapsed": 334,
"status": "ok",
"timestamp": 1759947959295,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "fdf01306-90e5-4c94-a8b2-e0aba3397f55",
"outputId": "c3569720-b2a1-4912-c8cf-aed2ca86e019"
},
"outputs": [],
"source": [
"#\n",
"# -------------------------------------------------------\n",
"# 0. Define the Year and Study Area\n",
"# -------------------------------------------------------\n",
"year = 2020\n",
"start_date = ee.Date.fromYMD(year, 1, 1)\n",
"end_date = start_date.advance(1, 'year')\n",
"\n",
"# Replace this with your own geometry (e.g., a FeatureCollection or ee.Geometry.Polygon)\n",
"geometry = clip_extent_GEE # ee.Geometry.BBox(10.0, 45.0, 11.0, 46.0) # Example: Northern Italy 100×100 km box\n",
"\n",
"# -------------------------------------------------------\n",
"# 1. Load Google Satellite Embeddings\n",
"# -------------------------------------------------------\n",
"# The embeddings are available as annual composites:\n",
"# 'GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL'\n",
"embeddings = ee.ImageCollection('GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL')\n",
"\n",
"# Filter by year and region\n",
"embeddings_filtered = embeddings \\\n",
" .filter(ee.Filter.date(start_date, end_date)) \\\n",
" .filterBounds(geometry)\n",
"\n",
"# Mosaic into a single image (each pixel = embedding vector)\n",
"embeddings_image = embeddings_filtered.mosaic()\n",
"\n",
"# Inspect the band names (should show e.g., \"embedding_0\", \"embedding_1\", …)\n",
"print(\"Embedding bands:\", embeddings_image.bandNames().getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c676a515-667b-4755-91e2-cbfa821bd11c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 63,
"status": "ok",
"timestamp": 1759947959386,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "c676a515-667b-4755-91e2-cbfa821bd11c",
"outputId": "fe0ce701-cdc6-4bec-9fef-ac1fea9e1059"
},
"outputs": [],
"source": [
"if boo_RunGE:\n",
" ## # -------------------------------------------------------\n",
" # # 2. Visual Sanity Check (optional, if running in Colab)\n",
" # # -------------------------------------------------------\n",
" # # If using geemap or folium, you can visualize:\n",
" # import geemap\n",
" # Map = geemap.Map()\n",
" # Map.centerObject(geometry, 9)\n",
" # Map.addLayer(embeddings_image.select(0), {'min': 0, 'max': 1}, 'Embedding Band 0')\n",
" # Map\n",
" print(\"map\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24e9429a-0ccb-4b86-b4d4-cfbc6fbfc339",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 50,
"status": "ok",
"timestamp": 1759947959440,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "24e9429a-0ccb-4b86-b4d4-cfbc6fbfc339",
"outputId": "09f3985b-c77a-409f-9bb1-b268a06f7fc9"
},
"outputs": [],
"source": [
"if boo_RunGE:\n",
" #\n",
" # -------------------------------------------------------\n",
" # 1. Load your GeoDataFrame\n",
" # -------------------------------------------------------\n",
" # Example: all_samples_gdf = gpd.read_file(\"all_samples.geojson\")\n",
"\n",
" # Required columns:\n",
" # geometry (shapely Point)\n",
" # block_id (int/str)\n",
" # forest_label (0/1)\n",
" #print(all_samples_gdf.head())\n",
"\n",
" # Assuming your dataframe is named `all_samples_gdf`\n",
" samples_with_id_gdf = all_samples_gdf.copy()\n",
"\n",
" # Sort by block_id (optional, but keeps order consistent)\n",
" samples_with_id_gdf = samples_with_id_gdf.sort_values(by=[\"block_id\"]).reset_index(drop=True)\n",
"\n",
" # Group by block_id and create local index within each block\n",
" samples_with_id_gdf[\"local_index\"] = samples_with_id_gdf.groupby(\"block_id\").cumcount() + 1\n",
"\n",
" # Create a zero-padded string (e.g., 0001, 0002, …)\n",
" samples_with_id_gdf[\"local_index_str\"] = samples_with_id_gdf[\"local_index\"].astype(str).str.zfill(4)\n",
"\n",
" # Concatenate block_id and local index → unique sample_id\n",
" samples_with_id_gdf[\"sample_id\"] = samples_with_id_gdf[\"block_id\"].astype(str) + \"_\" + samples_with_id_gdf[\"local_index_str\"]\n",
"\n",
" # Check result\n",
" print(samples_with_id_gdf[[\"block_id\", \"local_index\", \"sample_id\"]].head(10))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dbd31725-efc3-4379-b1f2-6738cbeda15d",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 49,
"status": "ok",
"timestamp": 1759948128683,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "dbd31725-efc3-4379-b1f2-6738cbeda15d",
"outputId": "27b0a957-b5ea-40de-a595-2ad7c29727be"
},
"outputs": [],
"source": [
"if boo_RunGE or boo_RunS1 or boo_RunS2:\n",
" #\n",
" block_ids = all_samples_gdf[\"block_id\"].unique()\n",
" results = [] # store all batch DataFrames here\n",
" batch_size = 50\n",
" print(f\"Number of blocks: {len(block_ids)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4550a47-b3aa-464e-b9a7-acce524c2fa3",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 53,
"status": "ok",
"timestamp": 1759947959549,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "b4550a47-b3aa-464e-b9a7-acce524c2fa3",
"outputId": "1fa3bfa7-5c48-45de-d378-e50228e3b011"
},
"outputs": [],
"source": [
"if boo_RunGE:\n",
" #\n",
" # Outer bar for blocks\n",
" import time\n",
" import random\n",
" for block_id in tqdm(block_ids, desc=\"Blocks\", position=0):\n",
" df_block = samples_with_id_gdf[all_samples_gdf[\"block_id\"] == block_id]\n",
" n_block = len(df_block)\n",
"\n",
" # Inner bar for batches within the block\n",
" for start in tqdm(range(0, n_block, batch_size),\n",
" desc=f\"Block {block_id} batches\",\n",
" position=1, # same \"line\" in terminal\n",
" leave=False): # clears inner bar after done\n",
" df_batch = df_block.iloc[start:start+batch_size]\n",
" # ... do your sampling here ...\n",
"\n",
"\n",
" # Construct EE FeatureCollection for the batch\n",
" fc_batch = ee.FeatureCollection([\n",
" ee.Feature(\n",
" ee.Geometry.Point([row['x'], row['y']]),\n",
" {'sample_id': row['sample_id'],\n",
" }\n",
" )\n",
" for _, row in df_batch.iterrows()\n",
" ])\n",
"\n",
" # Sample the batch\n",
" sampled_batch = embeddings_image.sampleRegions(\n",
" collection=fc_batch,\n",
" properties=['sample_id'],\n",
" scale=10,\n",
" geometries=True\n",
" )\n",
"\n",
" # Bring the sampled data back to Pandas\n",
" data = sampled_batch.getInfo()['features']\n",
" df_sampled = pd.DataFrame([\n",
" {**f['properties'],\n",
" 'lon': f['geometry']['coordinates'][0],\n",
" 'lat': f['geometry']['coordinates'][1]}\n",
" for f in data\n",
" ])\n",
"\n",
" results.append(df_sampled)\n",
"\n",
" # Sleep a small random time between 0.05s and 0.5s\n",
" time.sleep(random.uniform(0.05, 0.5))\n",
" ###\n",
" ###\n",
"\n",
" df_all_samples = pd.concat(results, ignore_index=True)\n",
" print(\"✅ Finished sampling all blocks\")\n",
" print(len(df_all_samples))\n",
" df_all_samples.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f864df6-3779-4e9c-8f3f-52b6f5b626f1",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 13,
"status": "ok",
"timestamp": 1759947959552,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "6f864df6-3779-4e9c-8f3f-52b6f5b626f1",
"outputId": "45ec6d7f-5712-4495-d5a9-8f6c7fcf379c"
},
"outputs": [],
"source": [
"if boo_RunGE:\n",
" #\n",
" # Save as CSV or Parquet\n",
" output_file_csv = \"GE_samples_2020.csv\"\n",
" output_file_parquet = \"GE_samples_2020.parquet\"\n",
"\n",
" # CSV (human-readable, slightly larger)\n",
" df_all_samples.to_csv(output_file_csv, index=False)\n",
"\n",
" # Parquet (smaller, preserves dtypes, faster to load)\n",
" df_all_samples.to_parquet(output_file_parquet, index=False)\n",
"\n",
" print(f\"✅ Finished sampling all blocks. Output saved as '{output_file_csv}' and '{output_file_parquet}'\")"
]
},
{
"cell_type": "markdown",
"id": "bcc9791d-cee8-476b-ac1f-5cdd08ae5b58",
"metadata": {
"id": "bcc9791d-cee8-476b-ac1f-5cdd08ae5b58"
},
"source": [
"### 🛰️ Sentinel 1 - SAR (VV/VH)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd35db89-cae6-40a3-b97b-fffbb3491268",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 596
},
"executionInfo": {
"elapsed": 11029,
"status": "ok",
"timestamp": 1759947970582,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "dd35db89-cae6-40a3-b97b-fffbb3491268",
"outputId": "85fce7bf-0c38-4279-f22e-fa055f25319f"
},
"outputs": [],
"source": [
"import datetime\n",
"\n",
"if boo_RunS1:\n",
" #\n",
"\n",
" step_days = 12 # equivalent to JS step\n",
"\n",
" # Date range\n",
" start_date = '2020-03-01'\n",
" end_date = '2020-10-30'\n",
"\n",
" # Load the Sentinel-1 GRD collection\n",
" s1 = ee.ImageCollection('COPERNICUS/S1_GRD') \\\n",
" .filterBounds(clip_extent_GEE) \\\n",
" .filterDate(start_date, end_date) \\\n",
" .filter(ee.Filter.eq('instrumentMode', 'IW')) \\\n",
" .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')) \\\n",
" .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))\n",
"\n",
" # Inspect the collection size\n",
" size = s1.size().getInfo()\n",
" print(f\"Number of images in collection: {size}\")\n",
"\n",
" # Optionally, print first few images to check properties\n",
" first_imgs = s1.limit(5).getInfo()\n",
" for i, img in enumerate(first_imgs['features']):\n",
" props = img['properties']\n",
" print(f\"Image {i+1}: date={props['system:time_start']}, pol={props.get('transmitterReceiverPolarisation')}\")\n",
"\n",
" # First, add acquisition date as milliseconds\n",
" def add_acq_ms(img):\n",
" idx = ee.String(img.get('system:index'))\n",
" date_str = idx.slice(17, 25) # YYYYMMDD\n",
" acq_date = ee.Date.parse('YYYYMMdd', date_str)\n",
" return img.set('acq_ms', acq_date.millis())\n",
"\n",
" s1_with_ms = s1.map(add_acq_ms)\n",
"\n",
" # Filter by numeric range (start and end in milliseconds)\n",
" start = ee.Date('2020-03-01').millis()\n",
" end = ee.Date('2020-03-30').millis()\n",
" s1_window = s1_with_ms.filter(ee.Filter.rangeContains('acq_ms', start, end))\n",
"\n",
" print(\"Images in window:\", s1_window.size().getInfo())\n",
"\n",
" # resetting to s1 but now with milli seconds attached\n",
" s1 = s1_with_ms\n",
"\n",
" def mask_edge(img):\n",
" mask = img.select(0).unitScale(-25, 5).multiply(255).toByte().connectedComponents(ee.Kernel.rectangle(1,1), 100)\n",
" return img # you can uncomment updateMask(mask.select(0)) if needed\n",
"\n",
" s1 = s1.map(mask_edge)\n",
"\n",
" # dB -> natural scale\n",
" def to_natural(img):\n",
" return ee.Image(10.0).pow(img.divide(10.0)).copyProperties(img, ['system:time_start'])\n",
"\n",
" s1 = s1.map(to_natural)\n",
"\n",
" # natural scale -> dB\n",
" def to_db(img):\n",
" return img.log10().multiply(10.0)\n",
" #s1 = s1.map(to_db)\n",
"\n",
" def daterange(start, end, step_days):\n",
" start_dt = datetime.datetime.strptime(start, \"%Y-%m-%d\")\n",
" end_dt = datetime.datetime.strptime(end, \"%Y-%m-%d\")\n",
" current = start_dt\n",
" while current < end_dt:\n",
" yield current, min(current + datetime.timedelta(days=step_days), end_dt)\n",
" current += datetime.timedelta(days=step_days)\n",
"\n",
" # Convert start/end to datetime\n",
" start_dt = datetime.datetime.strptime(start_date, \"%Y-%m-%d\")\n",
" end_dt = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n",
"\n",
" s1_images = []\n",
"\n",
" current = start_dt\n",
" for (d_start, d_end) in daterange(start_date, end_date, step_days):\n",
" start_ms = ee.Date(d_start.strftime(\"%Y-%m-%d\")).millis()\n",
" end_ms = ee.Date(d_end.strftime(\"%Y-%m-%d\")).millis()\n",
"\n",
" # Filter images by acq_ms\n",
" s1_window = s1_with_ms.filter(ee.Filter.rangeContains('acq_ms', start_ms, end_ms))\n",
"\n",
" num_images = s1_window.size().getInfo()\n",
" print(f\"{d_start} - {d_end} -> images: {num_images}\")\n",
"\n",
" # Only include periods with exactly 6 images\n",
" if num_images == 5:\n",
" mean_img = (s1_window\n",
" .mean()\n",
" .select(['VV','VH'],\n",
" [f'VV_{d_start.strftime(\"%Y%m%d\")}',\n",
" f'VH_{d_start.strftime(\"%Y%m%d\")}']))\n",
" s1_images.append(mean_img)\n",
" else:\n",
" print(f\"Skipping period {d_start} - {d_end}, expected 6 images, got {num_images}\")\n",
"\n",
" # Build the S1 stack\n",
" if s1_images:\n",
" s1_stack = ee.Image(s1_images[0])\n",
" for img in s1_images[1:]:\n",
" s1_stack = s1_stack.addBands(img)\n",
"\n",
" print(f\"Final S1 stack has {len(s1_stack.bandNames().getInfo())} bands\")\n",
"\n",
" # Smooth (reduce speckle)\n",
" s1_stack = s1_stack.reduceNeighborhood(\n",
" reducer=ee.Reducer.mean(),\n",
" kernel=ee.Kernel.rectangle(3,3)\n",
" ).clip(clip_extent_GEE)\n",
"\n",
" s1_stack = s1_stack.clip(clip_extent_GEE)\n",
"\n",
" # Remove first and last images in the S1 stack\n",
" full_s1_bands = s1_stack.bandNames().getInfo()[0:] # drop first and last by using[1:-1]\n",
" s1_stack_clean = s1_stack.select(full_s1_bands)\n",
" print(\"Number of S1 bands after removing incomplete periods:\", len(full_s1_bands))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe03beb7-a48b-4de4-a6a8-0c6f7b6180c6",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 4743,
"status": "ok",
"timestamp": 1759947975329,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "fe03beb7-a48b-4de4-a6a8-0c6f7b6180c6",
"outputId": "906f6cab-f0a4-4f2b-aafb-db3334719a1e"
},
"outputs": [],
"source": [
"if boo_RunS1:\n",
" #\n",
" # Separate VV and VH bands\n",
" vv_bands = [b for b in s1_stack.bandNames().getInfo() if b.startswith('VV')]\n",
" vh_bands = [b for b in s1_stack.bandNames().getInfo() if b.startswith('VH')]\n",
"\n",
" vv_stack = s1_stack.select(vv_bands)\n",
" vh_stack = s1_stack.select(vh_bands)\n",
"\n",
" # Compute temporal statistics\n",
" vv_mean = vv_stack.reduce(ee.Reducer.mean()).rename('VV_mean')\n",
" vv_std = vv_stack.reduce(ee.Reducer.stdDev()).rename('VV_std')\n",
" vv_min = vv_stack.reduce(ee.Reducer.min()).rename('VV_min')\n",
" vv_max = vv_stack.reduce(ee.Reducer.max()).rename('VV_max')\n",
"\n",
" vh_mean = vh_stack.reduce(ee.Reducer.mean()).rename('VH_mean')\n",
" vh_std = vh_stack.reduce(ee.Reducer.stdDev()).rename('VH_std')\n",
" vh_min = vh_stack.reduce(ee.Reducer.min()).rename('VH_min')\n",
" vh_max = vh_stack.reduce(ee.Reducer.max()).rename('VH_max')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "96c1e51b-c5dc-48d0-806c-161437b9ba45",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 2915,
"status": "ok",
"timestamp": 1759947978246,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "96c1e51b-c5dc-48d0-806c-161437b9ba45",
"outputId": "5b2d52ad-f60f-4ff2-e17c-b20216ffecb4"
},
"outputs": [],
"source": [
"if boo_RunS1:\n",
" #\n",
" s1_features = vv_mean.addBands([vv_std, vv_min, vv_max,\n",
" vh_mean, vh_std, vh_min, vh_max])\n",
"\n",
" print(\"S1 features bands:\", s1_features.bandNames().getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63e40019-3dfa-498d-912e-51727c793b1b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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]
},
"executionInfo": {
"elapsed": 251842,
"status": "ok",
"timestamp": 1759949878827,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "63e40019-3dfa-498d-912e-51727c793b1b",
"outputId": "48fa70f5-f291-4076-bc99-5ca6708d9e12"
},
"outputs": [],
"source": [
"if boo_RunS1:\n",
" #\n",
"\n",
" results = [] # store all batch DataFrames here\n",
" batch_size = 500 # Set > max points per block to process one block at a time\n",
" # change when connections time out (tested with 50)\n",
"\n",
" for block_id in tqdm(block_ids, desc=\"Blocks\", position=0):\n",
" df_block = samples_with_id_gdf[samples_with_id_gdf[\"block_id\"] == block_id]\n",
" n_block = len(df_block)\n",
"\n",
" # Filter the current block\n",
" current_block = blocks_gdf[blocks_gdf[\"block_id\"] == block_id]\n",
"\n",
" # Extract the geometry (GeoPandas)\n",
" block_geom = current_block.geometry.values[0] # shapely geometry\n",
"\n",
" # Convert to EE geometry\n",
" gee_block_geom = ee.Geometry.Polygon(list(block_geom.exterior.coords))\n",
"\n",
" # Now you can clip\n",
" s1_block = s1_features.clip(gee_block_geom)\n",
"\n",
" # Inner bar for batches within the block\n",
" for start in tqdm(range(0, n_block, batch_size),\n",
" desc=f\"Block {block_id} batches\",\n",
" position=1, # same \"line\" in terminal\n",
" leave=False): # clears inner bar after done\n",
" df_batch = df_block.iloc[start:start+batch_size]\n",
" # ... do your sampling here ...\n",
"\n",
" # Construct EE FeatureCollection for the batch\n",
" fc_batch = ee.FeatureCollection([\n",
" ee.Feature(\n",
" ee.Geometry.Point([row['x'], row['y']]),\n",
" {'sample_id': row['sample_id'],\n",
" }\n",
" )\n",
" for _, row in df_batch.iterrows()\n",
" ])\n",
"\n",
" # Sample the batch\n",
" sampled_batch = s1_block.sampleRegions(\n",
" collection=fc_batch,\n",
" properties=['sample_id'],\n",
" scale=10,\n",
" geometries=True\n",
" )\n",
"\n",
" # Bring the sampled data back to Pandas\n",
" data = sampled_batch.getInfo()['features']\n",
" df_sampled = pd.DataFrame([\n",
" {**f['properties'],\n",
" 'lon': f['geometry']['coordinates'][0],\n",
" 'lat': f['geometry']['coordinates'][1]}\n",
" for f in data\n",
" ])\n",
"\n",
" results.append(df_sampled)\n",
"\n",
" # Sleep a small random time between 0.05s and 0.5s\n",
" time.sleep(random.uniform(0.05, 0.5))\n",
" ###\n",
" ###\n",
"\n",
" df_S1_samples = pd.concat(results, ignore_index=True)\n",
" print(\"✅ Finished sampling all blocks\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b5cf623-ccb9-44bf-8b2d-3cc3833509c7",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 740,
"status": "ok",
"timestamp": 1759950366712,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "0b5cf623-ccb9-44bf-8b2d-3cc3833509c7",
"outputId": "a2fa4896-2670-4021-ef3d-0d09bf672068"
},
"outputs": [],
"source": [
"if boo_RunS1:\n",
" #\n",
" # Save as CSV or Parquet\n",
" output_file_csv = \"S1_samples_2020.csv\"\n",
" output_file_parquet = \"S1_samples_2020.parquet\"\n",
"\n",
" # CSV (human-readable, slightly larger)\n",
" df_S1_samples.to_csv(output_file_csv, index=False)\n",
"\n",
" # Parquet (smaller, preserves dtypes, faster to load)\n",
" df_S1_samples.to_parquet(output_file_parquet, index=False)\n",
"\n",
" print(f\"✅ Finished sampling all blocks. Output saved as '{output_file_csv}' and '{output_file_parquet}'\")"
]
},
{
"cell_type": "markdown",
"id": "09660685-e2b2-4ea4-8f78-c45404149e70",
"metadata": {
"id": "09660685-e2b2-4ea4-8f78-c45404149e70"
},
"source": [
"### 🌿 Sentinel-2 (Optical Indices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62d0b777-f87c-4ec8-bcb9-e77b954fe04b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 27,
"status": "ok",
"timestamp": 1759950416704,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "62d0b777-f87c-4ec8-bcb9-e77b954fe04b",
"outputId": "c2aff2d7-4c68-4648-ed41-160bc7b92da3"
},
"outputs": [],
"source": [
"if boo_RunS2:\n",
" #\n",
" #\n",
" year = 2020\n",
" start_date = '2020-01-01'\n",
" end_date = '2020-12-31'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "078d4777-5853-4868-bd09-23b478fe9aff",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 405,
"status": "ok",
"timestamp": 1759950417112,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "078d4777-5853-4868-bd09-23b478fe9aff",
"outputId": "fce21345-de9b-407b-fa76-dfae03662a89"
},
"outputs": [],
"source": [
"if boo_RunS2:\n",
" #\n",
" #\n",
" s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \\\n",
" .filterDate(start_date, end_date) \\\n",
" .filterBounds(clip_extent_GEE)\n",
"\n",
" csPlus = ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')\n",
" csPlusBands = csPlus.first().bandNames()\n",
" s2_linked = s2.linkCollection(csPlus, csPlusBands)\n",
"\n",
" def maskLowQA(img):\n",
" qaBand = 'cs'\n",
" clearThreshold = 0.6\n",
" mask = img.select(qaBand).gte(clearThreshold)\n",
" return img.updateMask(mask)\n",
"\n",
" s2_masked = s2_linked.map(maskLowQA).select('B.*')\n",
"\n",
" s2_masked_clipped = s2_masked.map(lambda img: img.clip(clip_extent_GEE))\n",
"\n",
" first_img = s2_masked_clipped.first()\n",
" print(first_img.bandNames().getInfo())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5c73137-0c3e-413d-bb86-3f5a2f702775",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 55
},
"executionInfo": {
"elapsed": 383,
"status": "ok",
"timestamp": 1759950417498,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "b5c73137-0c3e-413d-bb86-3f5a2f702775",
"outputId": "f486fe49-be79-4517-f896-679a9c069d8f"
},
"outputs": [],
"source": [
"if boo_RunS2:\n",
" #\n",
" # ------------------------------------------------------\n",
" # Function to compute per-image NDVI\n",
" # ------------------------------------------------------\n",
" def compute_ndvi_ic(s2_ic):\n",
" return s2_ic.map(lambda img: img.normalizedDifference(['B8','B4']).rename('NDVI'))\n",
"\n",
" # ------------------------------------------------------\n",
" # Function to compute per-image EVI\n",
" # ------------------------------------------------------\n",
" def compute_evi_ic(s2_ic):\n",
" return s2_ic.map(\n",
" lambda img: img.expression(\n",
" '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))',\n",
" {\n",
" 'NIR': img.select('B8'),\n",
" 'RED': img.select('B4'),\n",
" 'BLUE': img.select('B2')\n",
" }\n",
" ).rename('EVI')\n",
" )\n",
"\n",
" # ------------------------------------------------------\n",
" # Function to compute per-image NDWI (green-NIR)\n",
" # ------------------------------------------------------\n",
" def compute_ndwi_ic(s2_ic):\n",
" return s2_ic.map(lambda img: img.normalizedDifference(['B3','B8']).rename('NDWI'))\n",
"\n",
" # ------------------------------------------------------\n",
" # Function to compute per-image MSAVI2\n",
" # ------------------------------------------------------\n",
" def compute_msavi2_ic(s2_ic):\n",
" def msavi2(img):\n",
" NIR = img.select('B8')\n",
" RED = img.select('B4')\n",
" msavi2_img = NIR.multiply(2).add(1).subtract(\n",
" (NIR.multiply(2).add(1).pow(2).subtract(NIR.subtract(RED).multiply(8))).sqrt()\n",
" ).divide(2).rename('MSAVI2')\n",
" return img.addBands(msavi2_img)\n",
" return s2_ic.map(msavi2).select('MSAVI2')\n",
"\n",
" # ------------------------------------------------------\n",
" # Function to compute SWIR/NIR ratio\n",
" # ------------------------------------------------------\n",
" def compute_swir_nir_ic(s2_ic):\n",
" return s2_ic.map(lambda img: img.select('B11').divide(img.select('B8')).rename('SWIR_NIR'))\n",
"\n",
" # ------------------------------------------------------\n",
" # Function to compute mean, median, and robust amplitude (5–95%)\n",
" # ------------------------------------------------------\n",
" def temporal_stats(ic, band_name):\n",
" mean = ic.select(band_name).mean().rename(f'{band_name}_mean')\n",
" median = ic.select(band_name).median().rename(f'{band_name}_median')\n",
" p05 = ic.select(band_name).reduce(ee.Reducer.percentile([5])).rename(f'{band_name}_p05')\n",
" p95 = ic.select(band_name).reduce(ee.Reducer.percentile([95])).rename(f'{band_name}_p95')\n",
" amp_robust = p95.subtract(p05).rename(f'{band_name}_amp_robust')\n",
" return mean, median, amp_robust\n",
"\n",
" # ------------------------------------------------------\n",
" # Example usage - computing the ImageCollection for the Index\n",
" # ------------------------------------------------------\n",
" # Assume s2_masked is your cloud-masked, clipped S2 ImageCollection\n",
" ndvi_ic = compute_ndvi_ic(s2_masked_clipped)\n",
" evi_ic = compute_evi_ic(s2_masked_clipped)\n",
" ndwi_ic = compute_ndwi_ic(s2_masked_clipped)\n",
" msavi2_ic = compute_msavi2_ic(s2_masked_clipped)\n",
" swir_nir_ic = compute_swir_nir_ic(s2_masked_clipped)\n",
"\n",
" # Compute temporal stats for each index\n",
" ndvi_mean, ndvi_median, ndvi_amp = temporal_stats(ndvi_ic, 'NDVI')\n",
" evi_mean, evi_median, evi_amp = temporal_stats(evi_ic, 'EVI')\n",
" ndwi_mean, ndwi_median, ndwi_amp = temporal_stats(ndwi_ic, 'NDWI')\n",
" msavi2_mean, msavi2_median, msavi2_amp = temporal_stats(msavi2_ic, 'MSAVI2')\n",
" swir_nir_mean, swir_nir_median, swir_nir_amp = temporal_stats(swir_nir_ic, 'SWIR_NIR')\n",
"\n",
" # Stack all bands into a single S2 feature image\n",
" s2_features = ndvi_mean.addBands([ndvi_median, ndvi_amp,\n",
" evi_mean, evi_median, evi_amp,\n",
" ndwi_mean, ndwi_median, ndwi_amp,\n",
" msavi2_mean, msavi2_median, msavi2_amp,\n",
" swir_nir_mean, swir_nir_median, swir_nir_amp])\n",
"\n",
" print(\"S2 feature bands:\", s2_features.bandNames().getInfo())"
]
},
{
"cell_type": "markdown",
"id": "61e1efcd-f7e6-4006-91de-6f04720dfa79",
"metadata": {
"id": "61e1efcd-f7e6-4006-91de-6f04720dfa79"
},
"source": [
"```\n",
"msavi2_img = (\n",
" NIR.multiply(2).add(1)\n",
" .subtract(\n",
" (\n",
" NIR.multiply(2).add(1).pow(2)\n",
" .subtract(NIR.subtract(RED).multiply(8))\n",
" ).sqrt()\n",
" )\n",
" .divide(2)\n",
" .rename('MSAVI2')\n",
")\n",
"\n",
"```\n",
"\n",
"| Step | Code fragment | Mathematical term | Meaning |\n",
"| ---- | -------------------------------------- | -------------------------------------- | ---------------------------------------------------- |\n",
"| 1️⃣ | `NIR.multiply(2).add(1)` | ( 2NIR + 1 ) | Create the first term of the numerator |\n",
"| 2️⃣ | `.pow(2)` | ( (2NIR + 1)^2 ) | Square the first term |\n",
"| 3️⃣ | `NIR.subtract(RED).multiply(8)` | ( 8(NIR - RED) ) | Compute soil-adjusted difference between NIR and RED |\n",
"| 4️⃣ | `.subtract(...)` | ( (2NIR + 1)^2 - 8(NIR - RED) ) | Subtract the vegetation term from the squared term |\n",
"| 5️⃣ | `.sqrt()` | ( \\sqrt{(2NIR + 1)^2 - 8(NIR - RED)} ) | Take square root (dynamic soil correction) |\n",
"| 6️⃣ | `NIR.multiply(2).add(1).subtract(...)` | ( (2NIR + 1) - \\sqrt{\\dots} ) | Subtract the correction term from the base |\n",
"| 7️⃣ | `.divide(2)` | Divide by 2 | Normalize result to MSAVI2 range [−1, 1] |\n",
"| ✅ | `.rename('MSAVI2')` | — | Assign output band name |\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c963e021-ea98-4f9d-8412-a3d3eb56b891",
"metadata": {
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"84991d1f03dc4b65b6ffb2125d1dc17b",
"4adece1fcd4a4994bebe93be22e4296b",
"2dece5e475d3453baad94632d64d0d56",
"06273de61a8f45fdb14af272a90f4eeb",
"9a61b629049440f180dd6e1ffbae31fa",
"5be9f27a677b456782baa9bc96c15cb2",
"876e21899b464ffda9f398a768054963",
"c10743338e31498282a2914b978d0783",
"f2cc061be07c4cbc948b349b1c382fb0",
"bd7dff96588d47c1b65714e696a7fbbb",
"29e11ff6a25944b98b0d86ca533a343b",
"7544f7e4c7a14b46a752f26a8449fd7b",
"066e860e40294a50b792b52bb85953c1",
"3d4993a9b1054c8e9ad5141ba445e772",
"c05f898c19434b7c8b6fb09b2766b89b",
"59f27fdde29f4f2aa145d56b1f36091e",
"61b73cfff39e4af380f9fdab6eb4c09e",
"9d07781d64504c14a225bc2625532f57",
"c1e53da3d26c4298af3ba7013c52f042",
"8e6c4c47cbfa415a8e92eb89322beeb2",
"78cbe4b7f45644cc9c0f42a30ee975ee",
"055b973b87d14a59acf54aa4cd3c035f",
"d7b463a80d234cf1a83b902986f5898d",
"4446b76ab79d4af480767b36f04a3696",
"7cc7e9aff9004420950333c088dbdbbb",
"91d08ccb4ae742a4bdf7c7b268cce520",
"979017356a1441f2a90f5c0425b5ee38",
"8ed45dd50212416fb7c52bcdfe49d198",
"952c7bd1b43f4addbebf138a26dda254",
"f28f95dfd1dd4bc9a49b97cba289e5d3",
"da2a1e5ba69340c78b82ac075e236c7f",
"1c27db3639d24fc69ee9b35543582940",
"8a23b75d5dc84e6db3328a641b02dcae"
]
},
"executionInfo": {
"elapsed": 676044,
"status": "ok",
"timestamp": 1759953024881,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "c963e021-ea98-4f9d-8412-a3d3eb56b891",
"outputId": "75f19426-0f09-4bca-edf1-df3a8d1a22b8"
},
"outputs": [],
"source": [
"if boo_RunS2:\n",
" #\n",
" #\n",
" batch_size = 500 # Set > max points per block to process one block at a time\n",
" results = [] # store all batch DataFrames here\n",
"\n",
" for block_id in tqdm(block_ids, desc=\"Blocks\", position=0):\n",
" df_block = samples_with_id_gdf[samples_with_id_gdf[\"block_id\"] == block_id]\n",
" n_block = len(df_block)\n",
"\n",
" # Filter the current block\n",
" current_block = blocks_gdf[blocks_gdf[\"block_id\"] == block_id]\n",
"\n",
" # Extract the geometry (GeoPandas)\n",
" block_geom = current_block.geometry.values[0] # shapely geometry\n",
"\n",
" # Convert to EE geometry\n",
" gee_block_geom = ee.Geometry.Polygon(list(block_geom.exterior.coords))\n",
"\n",
" # Now you can clip\n",
" s1_block = s2_features.clip(gee_block_geom)\n",
"\n",
" # Inner bar for batches within the block\n",
" for start in tqdm(range(0, n_block, batch_size),\n",
" desc=f\"Block {block_id} batches\",\n",
" position=1, # same \"line\" in terminal\n",
" leave=False): # clears inner bar after done\n",
" df_batch = df_block.iloc[start:start+batch_size]\n",
" # ... do your sampling here ...\n",
"\n",
" # Construct EE FeatureCollection for the batch\n",
" fc_batch = ee.FeatureCollection([\n",
" ee.Feature(\n",
" ee.Geometry.Point([row['x'], row['y']]),\n",
" {'sample_id': row['sample_id'],\n",
" }\n",
" )\n",
" for _, row in df_batch.iterrows()\n",
" ])\n",
"\n",
" # Sample the batch\n",
" sampled_batch = s1_block.sampleRegions(\n",
" collection=fc_batch,\n",
" properties=['sample_id'],\n",
" scale=10,\n",
" geometries=True\n",
" )\n",
"\n",
" # Bring the sampled data back to Pandas\n",
" data = sampled_batch.getInfo()['features']\n",
" df_sampled = pd.DataFrame([\n",
" {**f['properties'],\n",
" 'lon': f['geometry']['coordinates'][0],\n",
" 'lat': f['geometry']['coordinates'][1]}\n",
" for f in data\n",
" ])\n",
"\n",
" results.append(df_sampled)\n",
"\n",
" # Sleep a small random time between 0.05s and 0.5s\n",
" time.sleep(random.uniform(0.05, 0.5))\n",
" ###\n",
" ###\n",
"\n",
" df_S2_samples = pd.concat(results, ignore_index=True)\n",
" print(\"✅ Finished sampling all blocks\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f18b521-7049-4bc7-8833-d28c389742cd",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 529,
"status": "ok",
"timestamp": 1759953043197,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "2f18b521-7049-4bc7-8833-d28c389742cd",
"outputId": "33d66cb0-35ab-4fbc-f99f-7bf1b71adc7e"
},
"outputs": [],
"source": [
"if boo_RunS2:\n",
" #\n",
" # Save as CSV or Parquet\n",
" output_file_csv = \"S2_samples_2020.csv\"\n",
" output_file_parquet = \"S2_samples_2020.parquet\"\n",
"\n",
" # CSV (human-readable, slightly larger)\n",
" df_S2_samples.to_csv(output_file_csv, index=False)\n",
"\n",
" # Parquet (smaller, preserves dtypes, faster to load)\n",
" df_S2_samples.to_parquet(output_file_parquet, index=False)\n",
"\n",
" print(f\"✅ Finished sampling all blocks. Output saved as '{output_file_csv}' and '{output_file_parquet}'\")"
]
},
{
"cell_type": "markdown",
"id": "f33f2b9c-1036-4393-9949-d07ba91af4a2",
"metadata": {
"id": "f33f2b9c-1036-4393-9949-d07ba91af4a2"
},
"source": [
"### 📦 Combining the data\n",
"\n",
"✅ Loaded 13523 samples with unique IDs.\n",
"| block_id | sample_id |\n",
"|:----------|:-------------|\n",
"| 003-009 | 003-009_0001 |\n",
"| 003-009 | 003-009_0002 |\n",
"| 003-009 | 003-009_0003 |\n",
"| 003-009 | 003-009_0004 |\n",
"| 003-009 | 003-009_0005 |\n",
"\n",
"\n",
"```\n",
"# Define your expected output files\n",
"files = {\n",
" \"GE\": \"GE_samples_2020.csv\",\n",
" \"S1\": \"S1_samples_2020.csv\",\n",
" \"S2\": \"S2_samples_2020.csv\"\n",
"}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48533585-10af-4194-a43d-01f9dc583c6e",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 89
},
"executionInfo": {
"elapsed": 27,
"status": "ok",
"timestamp": 1759953057239,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "48533585-10af-4194-a43d-01f9dc583c6e",
"outputId": "2cd8e980-6d34-43e4-9890-36e97449cb51"
},
"outputs": [],
"source": [
"samples_with_id_gdf.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71dbbfb5-214c-4485-87ad-5672f6f4ad61",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 915,
"status": "ok",
"timestamp": 1759953060322,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "71dbbfb5-214c-4485-87ad-5672f6f4ad61",
"outputId": "5262e12c-4cc5-4b26-cf55-09f11d774c78"
},
"outputs": [],
"source": [
"# Load sample IDs (all_samples_gdf already has sample_id)\n",
"all_samples_df = samples_with_id_gdf[['block_id', 'sample_id', 'x', 'y', 'Forest']].copy()\n",
"\n",
"# Load each dataset if the file exists\n",
"GE_df = pd.read_csv(files['GE']) if os.path.exists(files['GE']) else None\n",
"S1_df = pd.read_csv(files['S1']) if os.path.exists(files['S1']) else None\n",
"S2_df = pd.read_csv(files['S2']) if os.path.exists(files['S2']) else None\n",
"\n",
"# Rename coordinates in the master sample table\n",
"all_samples_df = all_samples_df.rename(columns={'x': 'lon', 'y': 'lat'})\n",
"\n",
"# Drop lat/lon from feature tables to avoid duplicates\n",
"GE_df = GE_df.drop(columns=['lat', 'lon'], errors='ignore')\n",
"S1_df = S1_df.drop(columns=['lat', 'lon'], errors='ignore')\n",
"S2_df = S2_df.drop(columns=['lat', 'lon'], errors='ignore')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48650dbb-815b-4cd5-bea4-dd1d0582c236",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 162
},
"executionInfo": {
"elapsed": 7,
"status": "ok",
"timestamp": 1759953065126,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "48650dbb-815b-4cd5-bea4-dd1d0582c236",
"outputId": "6c96f052-7d24-4fb7-a09e-a8746ca72d83"
},
"outputs": [],
"source": [
"GE_df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9f374b8-cc56-4b74-bf53-65a5f5709a41",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"executionInfo": {
"elapsed": 87,
"status": "ok",
"timestamp": 1759953067433,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "e9f374b8-cc56-4b74-bf53-65a5f5709a41",
"outputId": "3c511edf-93d4-46c9-b7e1-5a0d19f5c497"
},
"outputs": [],
"source": [
"S1_df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c35b4ea-9497-43dd-b8b4-1093088b1d1e",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 107
},
"executionInfo": {
"elapsed": 47,
"status": "ok",
"timestamp": 1759953075264,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "7c35b4ea-9497-43dd-b8b4-1093088b1d1e",
"outputId": "2ebd640b-16cf-4a70-e30e-6505b293f94c"
},
"outputs": [],
"source": [
"S2_df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d8efc5e6-9d33-468a-9129-4f297ca7aa83",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 560
},
"executionInfo": {
"elapsed": 9,
"status": "ok",
"timestamp": 1759953075277,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "d8efc5e6-9d33-468a-9129-4f297ca7aa83",
"outputId": "198ffab6-caf9-4c68-cc73-59e8ebc9376f"
},
"outputs": [],
"source": [
"# Merge datasets on sample_id\n",
"df_merged = all_samples_df.copy()\n",
"\n",
"if GE_df is not None:\n",
"\n",
" df_merged = df_merged.merge(GE_df, on='sample_id', how='left')\n",
"\n",
"if S1_df is not None:\n",
" df_merged = df_merged.merge(S1_df, on='sample_id', how='left')\n",
"\n",
"\n",
"if S2_df is not None:\n",
" df_merged = df_merged.merge(S2_df, on='sample_id', how='left')\n",
"\n",
"\n",
"print(f\"✅ Combined dataset shape: {df_merged.shape}\")\n",
"print(df_merged.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53e4cbb1-0558-4d74-b1ab-123c2b0c8afe",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 270
},
"executionInfo": {
"elapsed": 11,
"status": "ok",
"timestamp": 1759953075291,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "53e4cbb1-0558-4d74-b1ab-123c2b0c8afe",
"outputId": "376f72e5-9854-4490-efc9-2f9da909eea4"
},
"outputs": [],
"source": [
"df_merged.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4cb7dca5-a45b-4b13-a9ac-7846e357651c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 2547,
"status": "ok",
"timestamp": 1759953077836,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "4cb7dca5-a45b-4b13-a9ac-7846e357651c",
"outputId": "161633cd-3547-4e8f-9a22-f234ff465586"
},
"outputs": [],
"source": [
"feature_set_file = \"forest_feature_set_2020.csv\"\n",
"\n",
"df_merged.to_csv(feature_set_file, index=False)\n",
"print(f\"✅ Feature set saved to {feature_set_file}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13783ab5-b7bd-490e-9335-bab683d48424",
"metadata": {
"executionInfo": {
"elapsed": 104,
"status": "ok",
"timestamp": 1759953077984,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "13783ab5-b7bd-490e-9335-bab683d48424"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "910caa14-db00-4a09-9db8-e046801ee8ed",
"metadata": {
"id": "910caa14-db00-4a09-9db8-e046801ee8ed"
},
"source": [
"\n",
"## Land-cover modeling\n",
"\n",
"### Step 1: Data exploration and preparation\n",
"\n",
"We will handle:\n",
"- Load dataset and inspect basic info\n",
"- Identify feature types: embeddings (A00-A63), SAR indices (VH/VV), optical indices (NDVI, EVI, MSAVI2, etc.)\n",
"- Handle missing values\n",
"- Assign spatial blocks for GroupKFold cross-validation\n",
"\n",
"README / Notes on Imputation:\n",
"- Missing values are present only in some optical indices (NDVI, EVI, MSAVI2, NDWI, SWIR_NIR).\n",
"- Missing target or coordinates are not observed; if they existed, rows would be dropped.\n",
"- Feature imputation is done using sklearn's SimpleImputer:\n",
" * strategy='mean' for embeddings (A00-A63)\n",
" * strategy='median' for indices (SAR + optical features)\n",
"- This ensures that all 13,523 samples are retained for modelling.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fa756190-4f02-4d94-9e62-37c8b78ec37b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"executionInfo": {
"elapsed": 1203,
"status": "ok",
"timestamp": 1759953079086,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "fa756190-4f02-4d94-9e62-37c8b78ec37b",
"outputId": "0b354f04-651e-4747-b6a6-f70229b2f831"
},
"outputs": [],
"source": [
"# ---------- CONFIG ----------\n",
"FILEPATH = feature_set_file # change to your path\n",
"TARGET_COL = \"Forest\"\n",
"LON_COL = \"lon\"\n",
"LAT_COL = \"lat\"\n",
"N_SPLITS = 5\n",
"RANDOM_SEED = 42\n",
"\n",
"\n",
"# ---------- STEP 1: Load & inspect data ----------\n",
"print(\"Loading dataset...\")\n",
"df = pd.read_csv(FILEPATH)\n",
"print(f\"Dataset shape: {df.shape}\")\n",
"print(\"Columns:\", df.columns.tolist())\n",
"print(\"\\nSample rows:\")\n",
"print(df.head())\n",
"\n",
"\n",
"# ---------- STEP 2: Identify feature groups ----------\n",
"# Embeddings: A00-A63\n",
"embedding_cols = [f\"A{i:02d}\" for i in range(64)]\n",
"print(f\"Embedding columns ({len(embedding_cols)}): {embedding_cols[:5]} ... {embedding_cols[-5:]}\")\n",
"\n",
"\n",
"# Sentinel-1 SAR features\n",
"sar_cols = ['VH_max','VH_mean','VH_min','VH_std','VV_max','VV_mean','VV_min','VV_std']\n",
"print(f\"SAR columns ({len(sar_cols)}): {sar_cols}\")\n",
"\n",
"\n",
"# Sentinel-2 optical indices\n",
"optical_cols = ['EVI_amp_robust','EVI_mean','EVI_median','MSAVI2_amp_robust','MSAVI2_mean','MSAVI2_median',\n",
"'NDVI_amp_robust','NDVI_mean','NDVI_median','NDWI_amp_robust','NDWI_mean','NDWI_median',\n",
"'SWIR_NIR_amp_robust','SWIR_NIR_mean','SWIR_NIR_median']\n",
"print(f\"Optical index columns ({len(optical_cols)}): {optical_cols}\")\n",
"\n",
"\n",
"# Combine indices\n",
"index_cols = sar_cols + optical_cols\n",
"\n",
"\n",
"# ---------- STEP 3: Missing value check ----------\n",
"missing_counts = df[embedding_cols + index_cols].isna().sum().sort_values(ascending=False)\n",
"print(\"Missing values per feature (top 10):\")\n",
"print(missing_counts.head(10))\n",
"\n",
"\n",
"# Simple imputation\n",
"print(\"Imputing missing values...\")\n",
"if embedding_cols:\n",
" print(\" strategy='mean' ...\")\n",
" emb_imp = SimpleImputer(strategy=\"mean\")\n",
" df[embedding_cols] = emb_imp.fit_transform(df[embedding_cols])\n",
"if index_cols:\n",
" print(\" strategy='median' ...\")\n",
" idx_imp = SimpleImputer(strategy=\"median\")\n",
" df[index_cols] = idx_imp.fit_transform(df[index_cols])\n",
"\n",
"# ---------- STEP 4: Use existing 5x5km blocks ----------\n",
"# Each block_id corresponds to a 5×5 km area; no need to create new blocks\n",
"print(f\"Number of unique 5x5km blocks: {df['block_id'].nunique()}\")\n",
"\n",
"\n",
"# Create a numeric code for each unique block\n",
"df['block_id_code'] = pd.Categorical(df['block_id']).codes\n",
"\n",
"plt.figure(figsize=(8,6))\n",
"plt.scatter(df[LON_COL], df[LAT_COL], c=df['block_id_code'], cmap='tab20', s=6)\n",
"plt.xlabel(LON_COL)\n",
"plt.ylabel(LAT_COL)\n",
"plt.title(\"Spatial blocks (5x5 km)\")\n",
"plt.colorbar(label='block_id_code')\n",
"plt.show()\n",
"\n",
"\n",
"# ---------- STEP 5: Prepare GroupKFold splits ----------\n",
"gkf = GroupKFold(n_splits=N_SPLITS)\n",
"splits = list(gkf.split(X=df, y=df[TARGET_COL], groups=df['block_id']))\n",
"for i, (train_idx, test_idx) in enumerate(splits):\n",
" print(f\"Fold {i}: train {len(train_idx)}, test {len(test_idx)}\")\n",
"\n",
"\n",
"print(\"Data preparation complete. Next: modeling pipelines for E, I, and E+I datasets.\")\n"
]
},
{
"cell_type": "markdown",
"id": "dd6b822d-9103-42c4-9ad5-d2eff81c3dbd",
"metadata": {
"id": "dd6b822d-9103-42c4-9ad5-d2eff81c3dbd"
},
"source": [
"### Step 2: Modeling"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa87fed0-f3f2-4340-945e-dce1583bc8de",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 89
},
"executionInfo": {
"elapsed": 6,
"status": "ok",
"timestamp": 1759953079093,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "aa87fed0-f3f2-4340-945e-dce1583bc8de",
"outputId": "144f1adc-df1b-4135-e153-f2070d25738d"
},
"outputs": [],
"source": [
"# ======================================================\n",
"# ⚙️ File Management and Execution Flags\n",
"# ======================================================\n",
"\n",
"# Define expected key outputs\n",
"model_files = {\n",
" \"RF_MODEL\": r\"...rf_model_embeddings_full.pkl\",\n",
" \"RF_RESULTS\": r\"...\\rf_gridsearch_results_metrics.csv\",\n",
" \"RF_BESTPARAMS\": r\"...\\rf_best_params.json\",\n",
" \"RF_SUMMARY\": r\"...\\rf_model_summary_metrics.csv\",\n",
"}\n",
"\n",
"# Individual control for heavy computations\n",
"boo_RecreateModel = False # 🚀 if True, retrains RF even if model exists\n",
"\n",
"def should_run_models(dataset_key, recreate_flag=False):\n",
" \"\"\"Check if a dataset/model should be (re)computed based on file presence and overwrite flags.\"\"\"\n",
" file_path = model_files[dataset_key]\n",
" exists = os.path.exists(file_path)\n",
"\n",
" if exists and not overwrite and not recreate_flag:\n",
" print(f\"✅ {dataset_key}: {os.path.basename(file_path)} already exists — skipping.\")\n",
" return False\n",
" elif exists and (overwrite or recreate_flag):\n",
" print(f\"⚠️ {dataset_key}: {os.path.basename(file_path)} exists but overwrite/recreate=True — recomputing.\")\n",
" return True\n",
" else:\n",
" print(f\"🆕 {dataset_key}: {os.path.basename(file_path)} missing — computing now.\")\n",
" return True\n",
"\n",
"# ======================================================\n",
"# 🧭 Evaluate What to Run\n",
"# ======================================================\n",
"boo_RecreateModel = should_run_models(\"RF_SUMMARY\", recreate_flag=False) # set to False to skip\n",
"\n",
"print(\"\\nExecution Plan:\")\n",
"print(f\"Run the Statistical Models: {boo_RecreateModel}\")\n"
]
},
{
"cell_type": "markdown",
"id": "ecc1823a-5ee3-429e-ae94-7bfa78587d82",
"metadata": {
"id": "ecc1823a-5ee3-429e-ae94-7bfa78587d82"
},
"source": [
"#### KNeighborsClassifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbd648b4-60d1-44eb-aa26-e56de73032ca",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 234
},
"executionInfo": {
"elapsed": 1854,
"status": "ok",
"timestamp": 1759953080949,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "bbd648b4-60d1-44eb-aa26-e56de73032ca",
"outputId": "c5b06c0a-12b2-4575-fc4a-2c87b1d911f6"
},
"outputs": [],
"source": [
"if boo_RecreateModel:\n",
" #\n",
" # ---------- CONFIG ----------\n",
" N_NEIGHBORS = 5 # can be tuned\n",
" FEATURE_COLS = embedding_cols # E dataset\n",
" TARGET = TARGET_COL\n",
"\n",
" # Prepare results storage\n",
" results_list = []\n",
"\n",
" # Loop over GroupKFold splits\n",
" for fold, (train_idx, test_idx) in enumerate(splits):\n",
" X_train = df.loc[train_idx, FEATURE_COLS].values\n",
" y_train = df.loc[train_idx, TARGET].values\n",
" X_test = df.loc[test_idx, FEATURE_COLS].values\n",
" y_test = df.loc[test_idx, TARGET].values\n",
"\n",
" # Initialize kNN classifier\n",
" knn = KNeighborsClassifier(n_neighbors=N_NEIGHBORS)\n",
" knn.fit(X_train, y_train)\n",
"\n",
" # Predict probabilities for the positive class\n",
" y_prob = knn.predict_proba(X_test)[:, 1]\n",
"\n",
" # Compute metrics\n",
" auc = roc_auc_score(y_test, y_prob)\n",
" pr_auc = average_precision_score(y_test, y_prob)\n",
" brier = brier_score_loss(y_test, y_prob)\n",
"\n",
" # Store results\n",
" results_list.append({\n",
" 'fold': fold,\n",
" 'AUC': auc,\n",
" 'PR_AUC': pr_auc,\n",
" 'Brier': brier\n",
" })\n",
" print(f\"Fold {fold}: AUC={auc:.3f}, PR_AUC={pr_auc:.3f}, Brier={brier:.3f}\")\n",
"\n",
" # Aggregate results\n",
" results_df = pd.DataFrame(results_list)\n",
" mean_results = results_df.mean()\n",
" print(\"\\nAverage across folds:\")\n",
" print(mean_results)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a5dbc2f-1778-47a4-86cb-dd431251784d",
"metadata": {
"executionInfo": {
"elapsed": 32,
"status": "ok",
"timestamp": 1759953081007,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "8a5dbc2f-1778-47a4-86cb-dd431251784d"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e99e451-8fd3-4fe7-8d93-f4ab9d6a739b",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"executionInfo": {
"elapsed": 9116,
"status": "ok",
"timestamp": 1759953090125,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "0e99e451-8fd3-4fe7-8d93-f4ab9d6a739b",
"outputId": "6e212626-7302-4402-d902-6767dc8a1ccc"
},
"outputs": [],
"source": [
"if boo_RecreateModel:\n",
" #\n",
" # Example: kNN baseline for E (embeddings)\n",
" results_E, mean_E = knn_group_cv(df, feature_cols=embedding_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_neighbors=5,\n",
" plot_curves=True)\n",
"\n",
" # Example: kNN baseline for I (indices only: S1 + S2)\n",
" results_I, mean_I = knn_group_cv(df, feature_cols=index_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_neighbors=5,\n",
" plot_curves=True)\n",
"\n",
"\n",
" # Combine embeddings and indices\n",
" combined_cols = embedding_cols + index_cols\n",
"\n",
" # kNN baseline for E+I (combined features)\n",
" results_EI, mean_EI = knn_group_cv(df, feature_cols=combined_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_neighbors=5,\n",
" plot_curves=True)"
]
},
{
"cell_type": "markdown",
"id": "98782010-0352-48f5-83ae-887a629c5763",
"metadata": {
"id": "98782010-0352-48f5-83ae-887a629c5763"
},
"source": [
"#### RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b57dd8cc-3654-4196-a1be-c843d7030b1e",
"metadata": {
"executionInfo": {
"elapsed": 2,
"status": "ok",
"timestamp": 1759953090130,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "b57dd8cc-3654-4196-a1be-c843d7030b1e"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "10d443e4-2b6d-4a90-99d3-82f5a9c5c105",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"executionInfo": {
"elapsed": 178317,
"status": "ok",
"timestamp": 1759953268449,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "10d443e4-2b6d-4a90-99d3-82f5a9c5c105",
"outputId": "18c90c59-7792-4e8c-b622-2b2e6192be33"
},
"outputs": [],
"source": [
"if boo_RecreateModel:\n",
" # Random Forest for E (embeddings)\n",
" results_rf_E, mean_rf_E = rf_group_cv(df, feature_cols=embedding_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_estimators=200,\n",
" max_depth=None, plot_curves=True)\n",
"\n",
" # Random Forest for I (indices)\n",
" results_rf_I, mean_rf_I = rf_group_cv(df, feature_cols=index_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_estimators=200,\n",
" max_depth=None, plot_curves=True)\n",
"\n",
" # Random Forest for E+I (combined)\n",
" combined_cols = embedding_cols + index_cols\n",
" results_rf_EI, mean_rf_EI = rf_group_cv(df, feature_cols=combined_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_estimators=200,\n",
" max_depth=None, plot_curves=True)"
]
},
{
"cell_type": "markdown",
"id": "19230611-034a-4202-ab72-1f436ff8ab45",
"metadata": {
"id": "19230611-034a-4202-ab72-1f436ff8ab45"
},
"source": [
"#### XGBClassifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c23692f-c4e9-4b69-b26e-526968fd0791",
"metadata": {
"executionInfo": {
"elapsed": 14,
"status": "ok",
"timestamp": 1759953268497,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "4c23692f-c4e9-4b69-b26e-526968fd0791"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "882ac45a-d964-4193-8e10-cc7a19c9c0ac",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"executionInfo": {
"elapsed": 38996,
"status": "ok",
"timestamp": 1759953307516,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "882ac45a-d964-4193-8e10-cc7a19c9c0ac",
"outputId": "1f29308f-4b0b-4ac5-e64f-79c4a052961a"
},
"outputs": [],
"source": [
"if boo_RecreateModel:\n",
" # XGBoost for E (embeddings)\n",
" results_xgb_E, mean_xgb_E = xgb_group_cv(df, feature_cols=embedding_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_estimators=200,\n",
" max_depth=6, learning_rate=0.1, plot_curves=True)\n",
"\n",
" # XGBoost for I (indices)\n",
" results_xgb_I, mean_xgb_I = xgb_group_cv(df, feature_cols=index_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_estimators=200,\n",
" max_depth=6, learning_rate=0.1, plot_curves=True)\n",
"\n",
" # XGBoost for E+I (combined)\n",
" combined_cols = embedding_cols + index_cols\n",
" results_xgb_EI, mean_xgb_EI = xgb_group_cv(df, feature_cols=combined_cols, target_col=TARGET_COL,\n",
" group_col='block_id', n_splits=5, n_estimators=200,\n",
" max_depth=6, learning_rate=0.1, plot_curves=True)\n"
]
},
{
"cell_type": "markdown",
"id": "f7bd6bdd-28e0-4ec6-bfe2-e7c8c1846f86",
"metadata": {
"id": "f7bd6bdd-28e0-4ec6-bfe2-e7c8c1846f86"
},
"source": [
"#### Collating the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e4bfbcb-6f03-4970-8e12-0501144dc74a",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 367
},
"executionInfo": {
"elapsed": 69,
"status": "ok",
"timestamp": 1759953307591,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "7e4bfbcb-6f03-4970-8e12-0501144dc74a",
"outputId": "697caaff-7e20-480a-90f3-80194aa92d84"
},
"outputs": [],
"source": [
"summary_path = model_files[\"RF_SUMMARY\"]\n",
"\n",
"if boo_RecreateModel:\n",
" # Create a list of dictionaries for all scenarios\n",
" print(\"🧠 Recreating summary metrics table...\")\n",
" summary_list = [\n",
" # kNN\n",
" {'Algorithm': 'kNN', 'Features': 'Test 1: E', 'AUC': mean_E['AUC'], 'PR_AUC': mean_E['PR_AUC'], 'Brier': mean_E['Brier']},\n",
" {'Algorithm': 'kNN', 'Features': 'Test 2: I', 'AUC': mean_I['AUC'], 'PR_AUC': mean_I['PR_AUC'], 'Brier': mean_I['Brier']},\n",
" {'Algorithm': 'kNN', 'Features': 'Test 3: E+I', 'AUC': mean_EI['AUC'], 'PR_AUC': mean_EI['PR_AUC'], 'Brier': mean_EI['Brier']},\n",
"\n",
" # Random Forest\n",
" {'Algorithm': 'Random Forest', 'Features': 'Test 1: E', 'AUC': mean_rf_E['AUC'], 'PR_AUC': mean_rf_E['PR_AUC'], 'Brier': mean_rf_E['Brier']},\n",
" {'Algorithm': 'Random Forest', 'Features': 'Test 2: I', 'AUC': mean_rf_I['AUC'], 'PR_AUC': mean_rf_I['PR_AUC'], 'Brier': mean_rf_I['Brier']},\n",
" {'Algorithm': 'Random Forest', 'Features': 'Test 3: E+I', 'AUC': mean_rf_EI['AUC'], 'PR_AUC': mean_rf_EI['PR_AUC'], 'Brier': mean_rf_EI['Brier']},\n",
"\n",
" # XGBoost\n",
" {'Algorithm': 'XGBoost', 'Features': 'Test 1: E', 'AUC': mean_xgb_E['AUC'], 'PR_AUC': mean_xgb_E['PR_AUC'], 'Brier': mean_xgb_E['Brier']},\n",
" {'Algorithm': 'XGBoost', 'Features': 'Test 2: I', 'AUC': mean_xgb_I['AUC'], 'PR_AUC': mean_xgb_I['PR_AUC'], 'Brier': mean_xgb_I['Brier']},\n",
" {'Algorithm': 'XGBoost', 'Features': 'Test 3: E+I', 'AUC': mean_xgb_EI['AUC'], 'PR_AUC': mean_xgb_EI['PR_AUC'], 'Brier': mean_xgb_EI['Brier']},\n",
" ]\n",
"\n",
" # Convert to DataFrame\n",
" summary_df = pd.DataFrame(summary_list)\n",
" summary_df = summary_df.sort_values(by=['Features','Algorithm']).reset_index(drop=True)\n",
"\n",
" # --- Save to CSV ---\n",
" summary_df.to_csv(summary_path, index=False)\n",
" print(f\"✅ Model summary saved to {summary_path}\")\n",
"\n",
"else:\n",
" # --- Load existing summary ---\n",
" print(\"🧠 Recreating summary metrics table from file...\")\n",
" if os.path.exists(summary_path):\n",
" print(\"📄 Loading existing model summary table...\")\n",
" summary_df = pd.read_csv(summary_path)\n",
" else:\n",
" raise FileNotFoundError(f\"⚠️ Summary file not found at {summary_path}. \"\n",
" \"Set boo_RecreateModel=True to regenerate it.\")\n",
"\n",
"# --- Display nicely formatted table ---\n",
"try:\n",
" display(\n",
" summary_df.style\n",
" .set_properties(subset=['Algorithm','Features'], **{'text-align': 'left'})\n",
" .set_properties(subset=['AUC','PR_AUC','Brier'], **{'text-align': 'right'})\n",
" .highlight_max(subset=['AUC','PR_AUC'], color='lightgreen')\n",
" .highlight_min(subset=['Brier'], color='lightblue')\n",
" .format({'AUC': '{:.3f}', 'PR_AUC': '{:.3f}', 'Brier': '{:.3f}'})\n",
" )\n",
"except Exception:\n",
" print(summary_df)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "692dff7a-6efd-4274-9c71-d43257a8efe3",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 360
},
"executionInfo": {
"elapsed": 726,
"status": "ok",
"timestamp": 1759953308326,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "692dff7a-6efd-4274-9c71-d43257a8efe3",
"outputId": "e4f21bc9-69c9-4ee6-f443-99940ec3c0a8"
},
"outputs": [],
"source": [
"# Prepare data (assuming summary_df already exists)\n",
"summary_plot = summary_df.copy()\n",
"summary_plot['Features'] = summary_plot['Features'].replace({\n",
" 'Test 1: E': 'E',\n",
" 'Test 2: I': 'I',\n",
" 'Test 3: E+I': 'E+I'\n",
"})\n",
"\n",
"sns.set(style=\"whitegrid\")\n",
"fig, axes = plt.subplots(1, 3, figsize=(14,5))\n",
"\n",
"# Plot AUC\n",
"sns.barplot(ax=axes[0], x='Features', y='AUC', hue='Algorithm', data=summary_plot)\n",
"axes[0].set_ylim(0.7, 1.0)\n",
"axes[0].set_title('AUC')\n",
"axes[0].set_ylabel('')\n",
"\n",
"# Plot PR_AUC\n",
"sns.barplot(ax=axes[1], x='Features', y='PR_AUC', hue='Algorithm', data=summary_plot)\n",
"axes[1].set_ylim(0.7, 1.0)\n",
"axes[1].set_title('PR AUC')\n",
"axes[1].set_ylabel('')\n",
"\n",
"# Plot Brier\n",
"sns.barplot(ax=axes[2], x='Features', y='Brier', hue='Algorithm', data=summary_plot)\n",
"axes[2].set_title('Brier Score')\n",
"axes[2].set_ylabel('')\n",
"\n",
"# Remove legends from individual subplots\n",
"for ax in axes:\n",
" ax.get_legend().remove()\n",
"\n",
"# Add a single horizontal legend on top\n",
"handles, labels = axes[0].get_legend_handles_labels()\n",
"fig.legend(handles, labels, loc='upper center', ncol=3, frameon=False)\n",
"\n",
"plt.tight_layout(rect=[0, 0, 1, 0.9]) # leave space on top for legend\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "a7d02036-4c9c-4047-95ad-5fdb8869e267",
"metadata": {
"id": "a7d02036-4c9c-4047-95ad-5fdb8869e267"
},
"source": [
"### Key Takeaways from the Modeling Experiment\n",
"\n",
"1. **Embeddings capture forest/non-forest effectively** \n",
" - High AUC ( ~0.95) and PR AUC ( ~0.96) for embeddings-only models show that the Google Satellite Embeddings encode **strong discriminative information** for JRC forest labels. \n",
"\n",
"2. **S1/S2 indices are partially embedded** \n",
" - Including Sentinel-1 and Sentinel-2 indices (E+I) **does not significantly improve performance**. \n",
" - This suggests that the indices’ signal is likely already encoded in the embeddings. \n",
"\n",
"3. **Indices alone are informative but weaker** \n",
" - RF/XGBoost on indices-only yields AUC ~0.87, PR AUC ~0.87. \n",
" - Indices can still contribute in cases where embeddings are unavailable, but embeddings dominate classification power. \n",
"\n",
"4. **RF vs XGBoost** \n",
" - XGBoost gives only a **marginal improvement** over RF on embeddings. \n",
" - Given RF’s simplicity, interpretability, and potential for further tuning, **RF is the recommended model** for this problem. \n",
"\n",
"5. **Calibration is good for nonlinear models on embeddings** \n",
" - Brier scores ~0.09 indicate reliable probability estimates from RF/XGBoost, whereas kNN and indices-only models are less well-calibrated ( ~0.14–0.20). \n",
"\n",
"---\n",
"\n",
"### Overall Conclusion\n",
"\n",
"The learned satellite embeddings are highly effective for forest classification. For operational purposes, a **Random Forest on embeddings** offers the best combination of performance, calibration, and interpretability, with minimal benefit from adding indices or using XGBoost.\n"
]
},
{
"cell_type": "markdown",
"id": "9ff5edf4-6878-4631-b69a-262759f76cda",
"metadata": {
"id": "9ff5edf4-6878-4631-b69a-262759f76cda"
},
"source": [
"## Formal Hypothesis\n",
"\n",
"Let the Google Satellite embedding at pixel p be the 64-dimensional vector:\n",
"\n",
"**Xₚ ∈ R⁶⁴**\n",
"\n",
"and let the JRC forest label at pixel p be the binary variable:\n",
"\n",
"**Yₚ ∈ {0, 1}**\n",
"\n",
"(where Yₚ = 1 means \"forest\" and Yₚ = 0 means \"non-forest\" according to the EC-JRC Global Forest Cover 2020 (V2)).\n",
"\n",
"\n",
"We test the following hypotheses:\n",
"\n",
"**Null hypothesis (H₀):** Xₚ contains **no predictive information** about forest presence, i.e. no classifier trained on \\( X_p \\) performs better than a random or trivial baseline.\n",
"\n",
"**Alternative hypothesis (Hₐ):** Xₚ contains **predictive information** about forest presence, i.e. a classifier trained on \\( X_p \\) performs **significantly better than baseline**.\n",
"\n",
"\n",
"This is a standard **supervised classification test** for representational information: \n",
"if models trained on **Xₚ** can predict **Yₚ** with **AUC**, **PR AUC**, or **Brier scores** substantially better than a no-skill or random model, we reject **H₀** in favor of **Hₐ**.\n",
"\n",
"\n",
"---\n",
"\n",
"### Experimental Design Summary\n",
"\n",
"- **Input variable**: 64-dimensional embedding vector (**Xₚ**)) from the Google Satellite Embedding dataset. \n",
"- **Target variable**: Forest / Non-forest label (**Yₚ**)) from the EC-JRC Global Forest Cover map (2020, 10 m). \n",
"- **Baselines**:\n",
" - k-Nearest Neighbors (kNN): mimicking GEE’s default classifier. \n",
" - Logistic Regression: optional linear baseline. \n",
"- **Comparative models**:\n",
" - Random Forest (RF) \n",
" - XGBoost (XB)\n",
"- **Evaluation metrics**: AUC, PR AUC, and Brier score (calibration). \n",
"- **Cross-validation**: 5-fold *spatial* cross-validation grouped by 5×5 km block IDs.\n",
"\n",
"---\n",
"\n",
"### Decision Criterion\n",
"\n",
"If any classifier trained on embeddings **(Xₚ)** achieves:\n",
"\n",
"- **AUC > 0.8** \n",
"- **PR AUC > 0.8** \n",
"- **Brier score < 0.15**\n",
"\n",
"then the null hypothesis **H₀** can be **rejected**, confirming that the embeddings encode **ecologically meaningful information** related to forest cover.\n",
"\n",
"---\n",
"\n",
"### Empirical Outcome\n",
"\n",
"All models trained on embeddings achieved AUC ≈ 0.95 and PR AUC ≈ 0.96, far exceeding baseline performance. \n",
"We therefore **reject H₀** and conclude that the Google Satellite Embeddings contain **strong predictive information** about forest presence at 10 m resolution.\n"
]
},
{
"cell_type": "markdown",
"id": "e8e7d611-b8f1-45e7-b65a-ba6e118ceb62",
"metadata": {
"id": "e8e7d611-b8f1-45e7-b65a-ba6e118ceb62"
},
"source": [
"### Geospatial verification"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "240cec52-5574-4dcd-96a6-461ffb30e791",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 107
},
"executionInfo": {
"elapsed": 7819,
"status": "ok",
"timestamp": 1759953316150,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "240cec52-5574-4dcd-96a6-461ffb30e791",
"outputId": "e199fc62-249c-42e9-fc8d-333a74270eba"
},
"outputs": [],
"source": [
"\n",
"target_col=TARGET_COL\n",
"df_samples = df.copy() #feature_cols=embedding_cols\n",
"\n",
"# Feature matrix and labels (from your sampled pixels)\n",
"X_full = df_samples[embedding_cols].values\n",
"y_full = df_samples[target_col].values\n",
"\n",
"# Split your sampled pixels into train/validation sets\n",
"X_train, X_val, y_train, y_val = train_test_split(\n",
" X_full, y_full, test_size=0.2, random_state=42, stratify=y_full\n",
")\n",
"\n",
"# Train the model on training set\n",
"rf_model = RandomForestClassifier(\n",
" n_estimators=200,\n",
" max_depth=None,\n",
" random_state=42,\n",
" n_jobs=-1\n",
")\n",
"rf_model.fit(X_train, y_train)\n",
"\n",
"# Predict on validation set\n",
"y_prob = rf_model.predict_proba(X_val)[:,1]\n",
"y_pred = rf_model.predict(X_val)\n",
"\n",
"# Compute metrics\n",
"from sklearn.metrics import roc_auc_score, average_precision_score, brier_score_loss, accuracy_score\n",
"\n",
"auc = roc_auc_score(y_val, y_prob)\n",
"pr_auc = average_precision_score(y_val, y_prob)\n",
"brier = brier_score_loss(y_val, y_prob)\n",
"acc = accuracy_score(y_val, y_pred)\n",
"\n",
"print(f\"Accuracy: {acc:.3f}\")\n",
"print(f\"ROC AUC: {auc:.3f}\")\n",
"print(f\"PR AUC: {pr_auc:.3f}\")\n",
"print(f\"Brier score: {brier:.3f}\")\n",
"\n",
"# Save the trained model\n",
"model_path = r\"...\\rf_model_embeddings_full.pkl\"\n",
"joblib.dump(rf_model, model_path)\n",
"print(f\"Random Forest model saved to {model_path}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f90b5a3-02c6-48c9-ba7a-40d5866a8e02",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"executionInfo": {
"elapsed": 132,
"status": "ok",
"timestamp": 1759953316285,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "9f90b5a3-02c6-48c9-ba7a-40d5866a8e02",
"outputId": "770b80f3-fa11-4470-f8d1-67c195214842"
},
"outputs": [],
"source": [
"# -------------------------------\n",
"# Paths\n",
"# -------------------------------\n",
"model_path = r\"...\\rf_model_embeddings_full.pkl\"\n",
"results_csv = r\"...\\rf_gridsearch_results_metrics.csv\"\n",
"best_params_json = r\"...\\rf_best_params.json\"\n",
"\n",
"# -------------------------------\n",
"# 1. Prepare features and labels\n",
"# -------------------------------\n",
"df_samples = df.copy()\n",
"feature_columns = embedding_cols\n",
"target_col = TARGET_COL\n",
"\n",
"X_full = df_samples[feature_columns].values\n",
"y_full = df_samples[target_col].values\n",
"\n",
"# -------------------------------\n",
"# 2. Load or train RF model\n",
"# -------------------------------\n",
"if os.path.exists(model_path):\n",
" print(\"Loading Random Forest model from disk.\")\n",
" rf_final = joblib.load(model_path)\n",
"\n",
" # Load best params + metrics if available\n",
" if os.path.exists(best_params_json):\n",
" with open(best_params_json, \"r\") as f:\n",
" best_info = json.load(f)\n",
" print(f\"\\nBest hyperparameters by ROC AUC (loaded from file):\")\n",
" print(best_info[\"best_params_auc\"])\n",
" print(\"Metrics:\")\n",
" for k, v in best_info.items():\n",
" if k != \"best_params_auc\":\n",
" print(f\" {k}: {v:.3f}\")\n",
" else:\n",
" print(\"⚠️ No best_params JSON found. Model loaded without metadata.\")\n",
"\n",
"else:\n",
" print(\"Training Random Forest model (this can take ~40min)...\")\n",
"\n",
" # -------------------------------\n",
" # 2a. Define hyperparameter grid\n",
" # -------------------------------\n",
" param_grid = {\n",
" 'n_estimators': [200, 500, 800],\n",
" 'max_depth': [None, 20, 30],\n",
" 'min_samples_split': [2, 5, 10],\n",
" 'min_samples_leaf': [1, 5, 10],\n",
" 'class_weight': ['balanced']\n",
" }\n",
"\n",
" grid = list(ParameterGrid(param_grid))\n",
" print(f\"{len(grid)} hyperparameter combinations to try.\") # 81\n",
"\n",
" # -------------------------------\n",
" # 2b. Setup CV folds\n",
" # -------------------------------\n",
" cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
"\n",
" results = []\n",
"\n",
" # -------------------------------\n",
" # 2c. Loop over hyperparameter combinations with tqdm\n",
" # -------------------------------\n",
" for params in tqdm(grid, desc=\"Grid Search CV\"):\n",
" fold_metrics = {'auc': [], 'accuracy': [], 'pr_auc': [], 'brier': []}\n",
"\n",
" for train_idx, val_idx in cv.split(X_full, y_full):\n",
" X_train, X_val = X_full[train_idx], X_full[val_idx]\n",
" y_train, y_val = y_full[train_idx], y_full[val_idx]\n",
"\n",
" rf = RandomForestClassifier(**params, random_state=42, n_jobs=-1)\n",
" rf.fit(X_train, y_train)\n",
"\n",
" y_prob = rf.predict_proba(X_val)[:, 1]\n",
" y_pred = rf.predict(X_val)\n",
"\n",
" fold_metrics['auc'].append(roc_auc_score(y_val, y_prob))\n",
" fold_metrics['accuracy'].append(accuracy_score(y_val, y_pred))\n",
" fold_metrics['pr_auc'].append(average_precision_score(y_val, y_prob))\n",
" fold_metrics['brier'].append(brier_score_loss(y_val, y_prob))\n",
"\n",
" results.append({\n",
" 'params': params,\n",
" 'mean_auc': np.mean(fold_metrics['auc']),\n",
" 'mean_accuracy': np.mean(fold_metrics['accuracy']),\n",
" 'mean_pr_auc': np.mean(fold_metrics['pr_auc']),\n",
" 'mean_brier': np.mean(fold_metrics['brier'])\n",
" })\n",
"\n",
" # -------------------------------\n",
" # 3. Save full metrics\n",
" # -------------------------------\n",
" results_df = pd.DataFrame(results)\n",
" results_df.to_csv(results_csv, index=False)\n",
" print(f\"Full GridSearch metrics saved to {results_csv}\")\n",
"\n",
" # -------------------------------\n",
" # 4. Save best hyperparameters by ROC AUC\n",
" # -------------------------------\n",
" best_row_auc = results_df.loc[results_df['mean_auc'].idxmax()]\n",
" best_params_auc = best_row_auc['params']\n",
"\n",
" with open(best_params_json, 'w') as f:\n",
" json.dump({\n",
" \"best_params_auc\": best_params_auc,\n",
" \"mean_auc\": best_row_auc['mean_auc'],\n",
" \"mean_accuracy\": best_row_auc['mean_accuracy'],\n",
" \"mean_pr_auc\": best_row_auc['mean_pr_auc'],\n",
" \"mean_brier\": best_row_auc['mean_brier']\n",
" }, f, indent=4)\n",
" print(f\"Best hyperparameters by ROC AUC saved to {best_params_json}\")\n",
"\n",
" print(\"Best hyperparameters by ROC AUC:\")\n",
" print(best_params_auc)\n",
" print(\"Metrics:\",\n",
" best_row_auc[['mean_accuracy','mean_auc','mean_pr_auc','mean_brier']].to_dict())\n",
"\n",
" # -------------------------------\n",
" # 5. Train final model on full dataset\n",
" # -------------------------------\n",
" rf_final = RandomForestClassifier(**best_params_auc, random_state=42, n_jobs=-1)\n",
" rf_final.fit(X_full, y_full)\n",
"\n",
" # Save model\n",
" joblib.dump(rf_final, model_path)\n",
" print(f\"Final model trained on full dataset and saved to {model_path}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdf95aa9-c950-4e61-933f-44d8811f53fc",
"metadata": {
"executionInfo": {
"elapsed": 4,
"status": "ok",
"timestamp": 1759953316294,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "fdf95aa9-c950-4e61-933f-44d8811f53fc"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d1e3053-ee12-417b-8c82-07b1ca883de0",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"executionInfo": {
"elapsed": 296,
"status": "ok",
"timestamp": 1759953316596,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "2d1e3053-ee12-417b-8c82-07b1ca883de0",
"outputId": "c27427c6-f70d-47f0-f75e-99d7c753473a"
},
"outputs": [],
"source": [
"# --- 0. Load JRC image (forest = 1, non-forest = 0) ---\n",
"#image2020 = ee.ImageCollection('JRC/GFC2020/V2').mosaic().select('Map').unmask(0)\n",
"\n",
"#print(\"JRC bands:\", image2020.bandNames().getInfo())\n",
"\n",
"# --- Try loading band names ---\n",
"try:\n",
" bands = image2020.bandNames().getInfo()\n",
" print(\"✅ JRC bands (from existing image):\", bands)\n",
"\n",
"except Exception as e:\n",
" print(\"⚠️ Error reading band names:\", e)\n",
" print(\"Reinitializing image2020 from JRC/GFC2020/V2...\")\n",
"\n",
" # Fallback: load JRC Global Forest Cover dataset\n",
" image2020 = ee.ImageCollection('JRC/GFC2020/V2').mosaic().select('Map').unmask(0)\n",
"\n",
" # Confirm recovery\n",
" try:\n",
" bands = image2020.bandNames().getInfo()\n",
" print(\"✅ JRC bands (from reloaded image):\", bands)\n",
" except Exception as e2:\n",
" print(\"❌ Still unable to get band names:\", e2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f76d88c9-ea5d-4942-8435-a8f4774850f4",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 126
},
"executionInfo": {
"elapsed": 17,
"status": "ok",
"timestamp": 1759953316617,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "f76d88c9-ea5d-4942-8435-a8f4774850f4",
"outputId": "af1cad74-55c3-44ba-f745-25e25f79b4e0"
},
"outputs": [],
"source": [
"# Compute median n_nonforest\n",
"median_value = blocks_gdf['n_nonforest'].median()+10\n",
"\n",
"# Find the row whose n_nonforest is closest to the median\n",
"idx = (blocks_gdf['n_nonforest'] - median_value).abs().idxmin()\n",
"median_block = blocks_gdf.loc[idx]\n",
"\n",
"print(f\"Median n_nonforest: {median_value}\")\n",
"print(\"Block closest to median:\")\n",
"print(median_block[['block_id', 'n_forest', 'n_nonforest']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "beae9f48-4a5b-45a7-a9b0-481179e3f099",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 31,
"status": "ok",
"timestamp": 1759953316676,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "beae9f48-4a5b-45a7-a9b0-481179e3f099",
"outputId": "a0f576fd-4a08-4a5e-b8b2-f6d2feb2eb59"
},
"outputs": [],
"source": [
"block_geom_wgs = blocks_gdf.geometry.iloc[idx]\n",
"gee_block_geom = ee.Geometry.Polygon(list(block_geom_wgs.exterior.coords))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce9f711d-ce3d-464b-b67c-d0ef6c782b84",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 532
},
"executionInfo": {
"elapsed": 1988,
"status": "ok",
"timestamp": 1759953318668,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "ce9f711d-ce3d-464b-b67c-d0ef6c782b84",
"outputId": "bcd6a7fe-0ec5-4d8e-e48f-cdd31b97418d"
},
"outputs": [],
"source": [
"##cmap = plt.colormaps['Greens'].resampled(2)\n",
"cmap = ListedColormap(['lightgray', 'green']) # 0=non-forest, 1=forest\n",
"\n",
"# Ensure jrc_block has a valid projection\n",
"proj = image2020.projection().atScale(10)\n",
"\n",
"# Clip and unmask directly\n",
"jrc_block_fixed = image2020.clip(gee_block_geom).unmask(0).rename('Forest')\n",
"\n",
"jrc_block_fixed = jrc_block_fixed.set({'system:time_start': ee.Date('2020-12-31T23:59:00').millis()})\n",
"\n",
"# Open in xarray\n",
"ds_block = xr.open_dataset(\n",
" jrc_block_fixed,\n",
" engine='ee',\n",
" geometry=gee_block_geom,\n",
" scale=10,\n",
" projection=proj\n",
")\n",
"\n",
"# Select the Forest band\n",
"da_block = ds_block['Forest']\n",
"\n",
"# Convert to numpy array and remove the extra dimension\n",
"arr_block = da_block.values.squeeze() # removes singleton dimensions, shape becomes (height, width)\n",
"\n",
"# Flip vertically (north up) and horizontally (west left) as needed\n",
"arr_block = (np.fliplr(arr_block)) # flips both axes\n",
"\n",
"# Optional: transpose if you want x,y axes to match array columns/rows\n",
"arr_block = arr_block.T # only if needed\n",
"\n",
"# Check shape\n",
"print(arr_block.shape)\n",
"\n",
"# # Plot\n",
"plt.figure(figsize=(6,6))\n",
"# cmap = plt.cm.get_cmap('Greens', 2) # 2 discrete colors: 0=non-forest, 1=forest\n",
"# cmap = ListedColormap(['lightgray', 'green']) # 0=non-forest, 1=forest\n",
"plt.imshow(arr_block, cmap=cmap, origin='upper')\n",
"plt.colorbar(ticks=[0,1], label='Forest class (0=non-forest, 1=forest)')\n",
"plt.title('JRC Forest / Non-Forest')\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01c70b67-3c19-4b8a-af5d-a85396b46ecb",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 506
},
"executionInfo": {
"elapsed": 86773,
"status": "ok",
"timestamp": 1759953405445,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "01c70b67-3c19-4b8a-af5d-a85396b46ecb",
"outputId": "c1ddfa7a-4c63-4fe0-c51e-5e5dd6fbe55d"
},
"outputs": [],
"source": [
"nc_path = r\"...\\ds_GE_block.nc\"\n",
"\n",
"if os.path.exists(nc_path):\n",
" print(\"Loading NetCDF from disk.\")\n",
" ds_GE_block = xr.open_dataset(nc_path)\n",
"\n",
"else:\n",
" print(\"Downloading from GEE and saved to NetCDF.\")\n",
" # Ensure jrc_block has a valid projection\n",
" proj = embeddings_image.projection().atScale(10)\n",
"\n",
" # Clip and unmask directly\n",
" GE_block = embeddings_image.clip(gee_block_geom)\n",
"\n",
" GE_block = GE_block.set({'system:time_start': ee.Date('2020-12-31T23:59:00').millis()})\n",
"\n",
" # Open in xarray\n",
" ds_GE_block = xr.open_dataset(\n",
" GE_block,\n",
" engine='ee',\n",
" geometry=gee_block_geom,\n",
" scale=10,\n",
" projection=proj\n",
" )\n",
"\n",
" # Save with compression to reduce file size\n",
" ds_GE_block.to_netcdf(\n",
" nc_path,\n",
" format=\"NETCDF4\",\n",
" encoding={var: {'zlib': True, 'complevel': 4} for var in ds_GE_block.data_vars}\n",
" )\n",
" print(f\"File {nc_path} exists after saving?\", os.path.exists(nc_path))\n",
"\n",
"print(ds_GE_block)\n",
"\n",
"\n",
"lat = ds_GE_block.lat.values\n",
"lon = ds_GE_block.lon.values\n",
"print(\"lat[0], lat[-1]:\", lat[0], lat[-1])\n",
"print(\"lon[0], lon[-1]:\", lon[0], lon[-1])\n",
"\n",
"# Extract all embedding bands from your ds_GE_block\n",
"da_embeddings = ds_GE_block.to_array().squeeze() # shape: (band, lat, lon)\n",
"\n",
"# Convert to numpy and align orientation\n",
"arr_embeddings = da_embeddings.values\n",
"\n",
"# Flip vertically (north-up)\n",
"arr_embeddings = np.flip(arr_embeddings, axis=1) # axis=1 → lat dimension\n",
"\n",
"# Transpose lat/lon if needed to match mask orientation\n",
"arr_embeddings = np.transpose(arr_embeddings, (0, 2, 1)) # (band, y, x)\n",
"\n",
"# Optional sanity check\n",
"print(arr_embeddings.shape) # (n_bands, height, width)\n",
"\n",
"\n",
"\n",
"# # # Plot\n",
"# plt.figure(figsize=(6,6))\n",
"# # cmap = plt.cm.get_cmap('Greens', 2) # 2 discrete colors: 0=non-forest, 1=forest\n",
"# # cmap = ListedColormap(['lightgray', 'green']) # 0=non-forest, 1=forest\n",
"# plt.imshow(arr_block, cmap=cmap, origin='upper')\n",
"# plt.colorbar(ticks=[0,1], label='Forest class (0=non-forest, 1=forest)')\n",
"# plt.title('JRC Forest / Non-Forest')\n",
"# plt.axis('off')\n",
"# plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "be14b92d-d41b-499b-8248-673d4b070a60",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 521
},
"executionInfo": {
"elapsed": 821,
"status": "ok",
"timestamp": 1759953406272,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "be14b92d-d41b-499b-8248-673d4b070a60",
"outputId": "5bee2d84-0cad-44cb-fc5c-c0366755984b"
},
"outputs": [],
"source": [
"# Select tile A44 (index 44)\n",
"arr_a44 = arr_embeddings[44, :, :]\n",
"\n",
"# Plot\n",
"arr_a44 = np.flipud(arr_a44)\n",
"arr_a44 = np.fliplr(arr_a44)\n",
"\n",
"plt.figure(figsize=(6,6))\n",
"plt.imshow(arr_a44, cmap=\"viridis\", origin=\"upper\")\n",
"plt.title(\"A44 – Embedding Structure (North-Up / West-Left)\")\n",
"plt.axis(\"off\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a93f5b2-feb8-4863-9505-2593bbd01343",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 8,
"status": "ok",
"timestamp": 1759953406285,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "6a93f5b2-feb8-4863-9505-2593bbd01343",
"outputId": "cff50663-63d1-49f7-bf0e-b3b7048a6701"
},
"outputs": [],
"source": [
"X = arr_embeddings.reshape(arr_embeddings.shape[0], -1).T # (pixels, features)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2df6bf21-e051-4df6-b928-8bfec2466ec1",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"executionInfo": {
"elapsed": 10,
"status": "ok",
"timestamp": 1759953406299,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "2df6bf21-e051-4df6-b928-8bfec2466ec1",
"outputId": "b0c4f387-1814-4ea3-86f8-fc4ca1ff8ea3"
},
"outputs": [],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c05e27da-00ca-46ec-a039-5691c4133491",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 6434,
"status": "ok",
"timestamp": 1759953600486,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "c05e27da-00ca-46ec-a039-5691c4133491",
"outputId": "44039263-ddf8-48b5-f920-7e6a61cd8a4f"
},
"outputs": [],
"source": [
"# -------------------------------\n",
"# Flip embeddings north-up to match JRC mask\n",
"# -------------------------------\n",
"arr_embeddings_aligned = np.flip(arr_embeddings, axis=2) # axis=1 is latitude\n",
"\n",
"# Flip longitude (x-axis) if needed\n",
"arr_embeddings_aligned = np.fliplr(arr_embeddings_aligned) # flips axis=2 (longitude)\n",
"\n",
"# Flatten for prediction\n",
"X = arr_embeddings_aligned.reshape(arr_embeddings_aligned.shape[0], -1).T # pixels × features\n",
"\n",
"# Mask out NaNs\n",
"valid_mask = ~np.isnan(X).any(axis=1)\n",
"X_valid = X[valid_mask]\n",
"\n",
"# Predict\n",
"y_pred = rf_final.predict(X_valid) # your Random Forest\n",
"\n",
"# # Map back to 2D grid\n",
"height, width = arr_embeddings.shape[1], arr_embeddings.shape[2]\n",
"# y_grid = np.full((height*width,), np.nan)\n",
"# y_grid[valid_mask] = y_pred\n",
"# y_grid = y_grid.reshape((height, width))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0bffb0e-53e5-49f7-be65-7be0d728c6b2",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 3657,
"status": "ok",
"timestamp": 1759953608578,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "f0bffb0e-53e5-49f7-be65-7be0d728c6b2",
"outputId": "191ed620-f6b4-41f2-b3ea-886c242c940c"
},
"outputs": [],
"source": [
"# ----------------------------------------------------\n",
"# ✅ Align and flatten ground truth\n",
"# ----------------------------------------------------\n",
"# Ensure arr_block (ground truth) matches embedding grid\n",
"assert arr_block.shape == (height, width), \"Shape mismatch between JRC and embeddings!\"\n",
"\n",
"# Flatten\n",
"y_true = arr_block.flatten()\n",
"\n",
"# Replace NaN with 0 (non-forest)\n",
"y_true = np.nan_to_num(y_true, nan=0)\n",
"\n",
"# Apply same mask as embeddings\n",
"y_true_valid = y_true[valid_mask]\n",
"\n",
"# ----------------------------------------------------\n",
"# ✅ Compute Metrics\n",
"# ----------------------------------------------------\n",
"y_prob = rf_final.predict_proba(X_valid)[:, 1]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "557a8a64-1edc-4c51-9a71-5386ba99c7ec",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"executionInfo": {
"elapsed": 5068,
"status": "ok",
"timestamp": 1759953618947,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "557a8a64-1edc-4c51-9a71-5386ba99c7ec",
"outputId": "84a1a47c-9139-4b01-f2e3-0af64db6f8ad"
},
"outputs": [],
"source": [
"# Assuming you have y_true and y_prob\n",
"fpr, tpr, thresholds_roc = roc_curve(y_true_valid, y_prob)\n",
"precision, recall, thresholds_pr = precision_recall_curve(y_true_valid, y_prob)\n",
"\n",
"# Option 1: Maximize Youden’s J statistic (TPR - FPR)\n",
"# as the maximum value of the index may be used as a criterion\n",
"# for selecting the optimum cut-off point when a diagnostic test\n",
"j_scores = tpr - fpr\n",
"best_threshold_roc = thresholds_roc[np.argmax(j_scores)]\n",
"\n",
"# Option 2: Maximize F1-score\n",
"f1_scores = [f1_score(y_true_valid, (y_prob >= t).astype(int)) for t in thresholds_pr[:-1]]\n",
"best_threshold_f1 = thresholds_pr[np.argmax(f1_scores)]\n",
"\n",
"print(f\"Best threshold (Youden’s J): {best_threshold_roc:.3f}\")\n",
"print(f\"Best threshold (F1): {best_threshold_f1:.3f}, for F1 = {max(f1_scores):.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ec858ec-d5ea-4f9e-b1ba-dfba92398678",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"executionInfo": {
"elapsed": 53,
"status": "ok",
"timestamp": 1759953623095,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "0ec858ec-d5ea-4f9e-b1ba-dfba92398678",
"outputId": "05efe920-36d4-4cca-e78b-d61e789d8365"
},
"outputs": [],
"source": [
"max(f1_scores)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d68f6801-ab54-4d3c-a9b5-1acd84e1fd48",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 107
},
"executionInfo": {
"elapsed": 136,
"status": "ok",
"timestamp": 1759953625185,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "d68f6801-ab54-4d3c-a9b5-1acd84e1fd48",
"outputId": "b03818e3-4db2-445d-fe03-60747c1a58d1"
},
"outputs": [],
"source": [
"# ----------------------------------------------------\n",
"# ✅ Compute Metrics\n",
"# ----------------------------------------------------\n",
"y_pred = (y_prob >= best_threshold_f1).astype(int)\n",
"y_pred = (y_prob >= best_threshold_roc).astype(int)\n",
"\n",
"# Map back to 2D grid\n",
"height, width = arr_embeddings.shape[1], arr_embeddings.shape[2]\n",
"y_grid = np.full((height*width,), np.nan)\n",
"y_grid[valid_mask] = y_pred\n",
"y_grid = y_grid.reshape((height, width))\n",
"\n",
"acc = accuracy_score(y_true_valid, y_pred)\n",
"auc = roc_auc_score(y_true_valid, y_prob)\n",
"pr_auc = average_precision_score(y_true_valid, y_prob)\n",
"brier = brier_score_loss(y_true_valid, y_prob)\n",
"\n",
"print(\"📊 Validation metrics over this block:\")\n",
"print(f\" Accuracy: {acc:.3f}\")\n",
"print(f\" ROC AUC: {auc:.3f}\")\n",
"print(f\" PR AUC: {pr_auc:.3f}\")\n",
"print(f\" Brier: {brier:.3f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "224fbacc-544e-4e37-b2df-1bfe01352080",
"metadata": {
"executionInfo": {
"elapsed": 64,
"status": "ok",
"timestamp": 1759953636216,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "224fbacc-544e-4e37-b2df-1bfe01352080"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4bc1bd9a-5ded-49a3-9b6e-03e219f89ba5",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"executionInfo": {
"elapsed": 979,
"status": "ok",
"timestamp": 1759953637197,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "4bc1bd9a-5ded-49a3-9b6e-03e219f89ba5",
"outputId": "575b1d68-b965-4715-baa5-067aaac1df74"
},
"outputs": [],
"source": [
"plt.figure(figsize=(12,5))\n",
"plt.subplot(1,2,1)\n",
"plt.title(\"Predicted forest mask\")\n",
"plt.imshow(y_grid, cmap='Greens', origin='upper')\n",
"plt.subplot(1,2,2)\n",
"plt.title(\"JRC ground truth\")\n",
"plt.imshow(arr_block, cmap='Greens', origin='upper')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c760bf0-d79c-4779-b933-3b5a214ffc0e",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 445
},
"executionInfo": {
"elapsed": 695,
"status": "ok",
"timestamp": 1759953637895,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "1c760bf0-d79c-4779-b933-3b5a214ffc0e",
"outputId": "d4f136d1-c15a-45e1-e35a-b7262a36480d"
},
"outputs": [],
"source": [
"# -----------------------------\n",
"# Thresholds and colors\n",
"# -----------------------------\n",
"f1_thresh = 0.2\n",
"youden_thresh = 0.6\n",
"boundaries = [0, f1_thresh, 0.3, 0.4, 0.5, youden_thresh, 1] # 7 boundaries → 6 intervals\n",
"\n",
"# Use top green from 'Greens' colormap\n",
"cmap_base = plt.colormaps['Greens']\n",
"green_rgba = cmap_base(1.0)\n",
"green_hex = \"#{:02x}{:02x}{:02x}\".format(int(green_rgba[0]*255),\n",
" int(green_rgba[1]*255),\n",
" int(green_rgba[2]*255))\n",
"\n",
"colors = [\"#ffffff\", \"#d9f0d3\", \"#a6d96a\", \"#66bd63\", \"#1a9850\", green_hex]\n",
"cmap = ListedColormap(colors)\n",
"norm = BoundaryNorm(boundaries, cmap.N)\n",
"\n",
"# -----------------------------\n",
"# Prepare predicted probability grid\n",
"# -----------------------------\n",
"# Flattened X → predicted probabilities y_prob\n",
"height, width = arr_embeddings_aligned.shape[1], arr_embeddings_aligned.shape[2]\n",
"y_prob_grid = np.full((height*width,), np.nan)\n",
"y_prob_grid[valid_mask] = y_prob\n",
"y_prob_grid = y_prob_grid.reshape((height, width))\n",
"\n",
"# -----------------------------\n",
"# Plotting\n",
"# -----------------------------\n",
"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
"\n",
"# --- Predicted probability map ---\n",
"ax = axes[0]\n",
"im = ax.imshow(y_prob_grid, cmap=cmap, norm=norm, origin='upper',\n",
" interpolation='none', extent=[0, width, 0, height])\n",
"ax.set_title(\"Predicted forest probability\")\n",
"\n",
"# Gridlines\n",
"ax.set_xticks(np.arange(0, width+1, 100))\n",
"ax.set_yticks(np.arange(0, height+1, 100))\n",
"ax.grid(color='gray', linestyle='--', linewidth=0.5)\n",
"ax.tick_params(length=0)\n",
"#ax.axis('off')\n",
"\n",
"# Hide tick labels but keep gridlines\n",
"#ax.set_xticklabels([])\n",
"ax.set_yticklabels([])\n",
"\n",
"# Colorbar on the left\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"left\", size=\"5%\", pad=0.1)\n",
"cbar = fig.colorbar(im, cax=cax, orientation='vertical')\n",
"cbar.set_label(\"Forest probability, with thresholds\")\n",
"\n",
"# Get the default ticks\n",
"ticks = cbar.get_ticks()\n",
"\n",
"# Create labels: default numeric labels, but replace F1/Youden with annotated strings\n",
"tick_labels = [f\"[F1] {f1_thresh:.2f}\" if t==f1_thresh\n",
" else f\"[Youden] {youden_thresh:.2f}\" if t==youden_thresh\n",
" else f\"{t:.2f}\"\n",
" for t in ticks]\n",
"\n",
"cbar.set_ticklabels(tick_labels)\n",
"\n",
"cax.yaxis.set_ticks_position('left')\n",
"cax.yaxis.set_label_position('left')\n",
"\n",
"# --- Ground truth map ---\n",
"ax = axes[1]\n",
"ax.imshow(arr_block, cmap='Greens', origin='upper', interpolation='none', extent=[0, width, 0, height])\n",
"ax.set_title(\"JRC ground truth (binary [0/1])\")\n",
"\n",
"# Matching gridlines\n",
"ax.set_xticks(np.arange(0, width+1, 100))\n",
"ax.set_yticks(np.arange(0, height+1, 100))\n",
"ax.grid(color='gray', linestyle='--', linewidth=0.5)\n",
"ax.tick_params(length=0)\n",
"#ax.axis('off')\n",
"\n",
"#plt.tight_layout()\n",
"plt.subplots_adjust(wspace=-0.125) # smaller number → closer, larger → further apart\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c8078ec-8e96-4bf3-8fde-09547787e448",
"metadata": {
"executionInfo": {
"elapsed": 6,
"status": "ok",
"timestamp": 1759953637904,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "3c8078ec-8e96-4bf3-8fde-09547787e448"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "ddee8908-81c6-4657-ad28-710bdacb571c",
"metadata": {
"id": "ddee8908-81c6-4657-ad28-710bdacb571c"
},
"source": [
"## The Investigation - \"Probing the Black Box: What Our AI Model Actually Learned\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "040816a1-2685-49a6-b2d6-78ff6e0f3a14",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"executionInfo": {
"elapsed": 6,
"status": "ok",
"timestamp": 1759953637913,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "040816a1-2685-49a6-b2d6-78ff6e0f3a14",
"outputId": "8733953e-4af6-4d8c-fb90-c6c7b8796590"
},
"outputs": [],
"source": [
"# Features: embeddings only\n",
"embedding_cols = [c for c in df.columns if c.startswith('A')]\n",
"\n",
"# Target indices (S1 + S2 derived)\n",
"index_cols = [\n",
" 'NDVI_mean', 'EVI_mean', 'MSAVI2_mean', 'NDWI_mean', 'SWIR_NIR_mean'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6af1d984-597e-4137-8cc7-e03ef4966495",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 758,
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},
"executionInfo": {
"elapsed": 2875891,
"status": "ok",
"timestamp": 1759956513802,
"user": {
"displayName": "Gijs van den Dool",
"userId": "05465556262142366614"
},
"user_tz": -120
},
"id": "6af1d984-597e-4137-8cc7-e03ef4966495",
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"outputs": [],
"source": [
"# ---- Feature lists ----\n",
"embedding_cols = [c for c in df.columns if c.startswith('A')]\n",
"index_cols = ['NDVI_mean', 'EVI_mean', 'MSAVI2_mean', 'NDWI_mean', 'SWIR_NIR_mean']\n",
"\n",
"# ---- Model setup ----\n",
"# Long Process - be aware!\n",
"# For faster run time, we can lower the n_estimators and limit their depth (max_depth).\n",
"# The resulting R² scores might be slightly lower, but the overall conclusion of the\n",
"# analysis will be identical.\n",
"rf_reg = RandomForestRegressor(\n",
" n_estimators=200,\n",
" # max_depth=20, # Prevents trees from becoming excessively deep\n",
" n_jobs=-1,\n",
" random_state=42\n",
")\n",
"\n",
"kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
"results = []\n",
"\n",
"# ---- Outer loop over each index ----\n",
"for target in tqdm(index_cols, desc=\"Predicting mean indices from embeddings\", position=0):\n",
" y = df[target].values\n",
" X = df[embedding_cols].values\n",
"\n",
" r2_scores = []\n",
"\n",
" # ---- Inner loop over folds ----\n",
" for fold_idx, (train_idx, test_idx) in enumerate(\n",
" tqdm(kf.split(X), total=kf.get_n_splits(), desc=f\"{target} folds\", position=1, leave=False)\n",
" ):\n",
" rf_reg.fit(X[train_idx], y[train_idx])\n",
" y_pred = rf_reg.predict(X[test_idx])\n",
" r2 = r2_score(y[test_idx], y_pred)\n",
" r2_scores.append(r2)\n",
"\n",
" results.append({\n",
" 'Index': target,\n",
" 'R2_mean': np.mean(r2_scores),\n",
" 'R2_std': np.std(r2_scores)\n",
" })\n",
"\n",
"index_r2_df = pd.DataFrame(results).sort_values(by='R2_mean', ascending=False)\n",
"\n",
"# ---- Display results ----\n",
"print(\"\\nAverage R² per index (sorted):\")\n",
"display(index_r2_df)\n",
"\n",
"# ---- Plot results ----\n",
"plt.figure(figsize=(8, 5))\n",
"plt.barh(index_r2_df['Index'], index_r2_df['R2_mean'], xerr=index_r2_df['R2_std'], color='seagreen', alpha=0.8)\n",
"plt.gca().invert_yaxis()\n",
"plt.xlabel('Mean R² (5-fold CV)')\n",
"plt.title('Predicting Mean Sentinel Indices from Google Satellite Embeddings')\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
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"cell_type": "markdown",
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"source": [
"### Analysis of Indices Encoded in Google Satellite Embeddings\n",
"\n",
"#### 1. The \"Strongly Encoded\" Indices: NDVI & NDWI\n",
"- **Result:** R²_mean of 0.82 for NDVI and 0.79 for NDWI \n",
"- **Interpretation:** This is a very strong result. Approximately 80% of the variance in NDVI and NDWI can be predicted using the embeddings alone. This provides concrete evidence that the embeddings have effectively learned the concepts of vegetation \"greenness\" (NDVI) and surface water content/vegetation moisture (NDWI). \n",
"- **Significance:** This explains why adding these indices to the classifier provided no significant performance boost — the information was already captured in a more complex, high-dimensional form by the embeddings. The low standard deviation (R²_std) indicates this finding is stable and reliable across the dataset.\n",
"\n",
"#### 2. The \"Partially Encoded\" Indices: MSAVI2 & SWIR/NIR\n",
"- **Result:** R²_mean of 0.72 for MSAVI2 and 0.55 for SWIR/NIR \n",
"- **Interpretation:** These indices are represented in the embeddings, but not as completely as NDVI/NDWI. MSAVI2 corrects for soil background influence, and SWIR/NIR bands are sensitive to moisture and subtle vegetation structure. The embeddings have learned some, but not all, of this information. \n",
"- **Significance:** While these indices might contain some unique signal not fully captured by the embeddings, it is not strong enough to substantially improve a classifier already leveraging the dominant information in the embeddings. The higher standard deviation suggests the relationship is less consistent across the dataset.\n",
"\n",
"#### 3. The \"Not Encoded\" Index: EVI\n",
"- **Result:** R²_mean of -0.48 \n",
"- **Interpretation:** A negative R² indicates that predicting EVI from embeddings performs worse than simply predicting the mean value. The model fails to capture a meaningful relationship between embeddings and EVI. \n",
"\n",
"**Possible reasons:**\n",
"- **Complexity:** EVI is more complex than NDVI; it incorporates the blue band for atmospheric correction and includes soil adjustment factors. The embeddings likely did not capture this multi-band relationship. \n",
"- **Irrelevance to the Task:** The embeddings are optimized for general-purpose representation, which aligns well with forest classification. If EVI’s information is not critical for this task, the embeddings would have no incentive to encode it. \n",
"\n",
"- **Significance:** This demonstrates that while embeddings are powerful, they are not universally omniscient. They capture dominant and useful signals (like NDVI) but may ignore more nuanced indices like EVI.\n"
]
},
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"source": [
"### Combine RF importance and Lasso coefficients for NDVI"
]
},
{
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"source": [
"# === Configuration ===\n",
"TARGET_INDEX = 'NDVI_mean'\n",
"FEATURES = embedding_cols\n",
"N_SPLITS = 5\n",
"RANDOM_STATE = 42\n",
"\n",
"# === Prepare data ===\n",
"X = df[FEATURES].values\n",
"y = df[TARGET_INDEX].values\n",
"\n",
"# === Initialize model ===\n",
"rf = RandomForestRegressor(\n",
" n_estimators=300,\n",
" max_depth=None,\n",
" n_jobs=-1,\n",
" random_state=RANDOM_STATE\n",
")\n",
"\n",
"kf = KFold(n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_STATE)\n",
"\n",
"importances = np.zeros((N_SPLITS, X.shape[1]))\n",
"r2_scores = []\n",
"\n",
"# === Cross-validation loop ===\n",
"for i, (train_idx, test_idx) in enumerate(tqdm(kf.split(X), total=N_SPLITS, desc=f\"RF folds for {TARGET_INDEX}\")):\n",
" rf.fit(X[train_idx], y[train_idx])\n",
" y_pred = rf.predict(X[test_idx])\n",
" r2_scores.append(r2_score(y[test_idx], y_pred))\n",
" importances[i, :] = rf.feature_importances_\n",
"\n",
"# === Aggregate results ===\n",
"rf_importance_mean = importances.mean(axis=0)\n",
"rf_importance_std = importances.std(axis=0)\n",
"rf_r2_mean = np.mean(r2_scores)\n",
"\n",
"# === Store feature importances ===\n",
"rf_feat_imp = pd.DataFrame({\n",
" 'Embedding': FEATURES,\n",
" 'Importance_mean': rf_importance_mean,\n",
" 'Importance_std': rf_importance_std\n",
"}).sort_values('Importance_mean', ascending=False)\n",
"\n",
"# === Display results ===\n",
"print(f\"Average R² across folds: {rf_r2_mean:.3f}\")\n",
"\n",
"plt.figure(figsize=(10,5))\n",
"plt.bar(range(10), rf_feat_imp['Importance_mean'].iloc[:10],\n",
" yerr=rf_feat_imp['Importance_std'].iloc[:10],\n",
" color='forestgreen', alpha=0.7)\n",
"plt.xticks(range(10), rf_feat_imp['Embedding'].iloc[:10], rotation=45)\n",
"plt.title(f'Top 10 Embedding Features Predicting {TARGET_INDEX} (Random Forest)')\n",
"plt.ylabel('Mean Importance')\n",
"plt.grid(alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
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"source": [
"# === Configuration ===\n",
"TARGET_INDEX = 'NDVI_mean'\n",
"FEATURES = embedding_cols\n",
"N_SPLITS = 5\n",
"RANDOM_STATE = 42\n",
"\n",
"# === Prepare data ===\n",
"X = df[FEATURES].values\n",
"y = df[TARGET_INDEX].values\n",
"\n",
"# Standardize features (important for Lasso)\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"# === Initialize model ===\n",
"# === Initialize LassoCV with higher max_iter ===\n",
"lasso = LassoCV(\n",
" alphas=np.logspace(-4, 1, 100),\n",
" cv=5,\n",
" random_state=RANDOM_STATE,\n",
" n_jobs=-1,\n",
" max_iter=5000, # increase iterations for convergence\n",
" tol=1e-4 # optional: adjust tolerance\n",
")\n",
"\n",
"kf = KFold(n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_STATE)\n",
"\n",
"coeffs = np.zeros((N_SPLITS, X_scaled.shape[1]))\n",
"r2_scores = []\n",
"\n",
"# === Cross-validation loop ===\n",
"for i, (train_idx, test_idx) in enumerate(tqdm(kf.split(X_scaled), total=N_SPLITS, desc=f\"Lasso folds for {TARGET_INDEX}\")):\n",
" lasso.fit(X_scaled[train_idx], y[train_idx])\n",
" y_pred = lasso.predict(X_scaled[test_idx])\n",
" r2_scores.append(r2_score(y[test_idx], y_pred))\n",
" coeffs[i, :] = lasso.coef_\n",
"\n",
"# === Aggregate results ===\n",
"lasso_coef_mean = coeffs.mean(axis=0)\n",
"lasso_coef_std = coeffs.std(axis=0)\n",
"lasso_r2_mean = np.mean(r2_scores)\n",
"\n",
"# === Store coefficients ===\n",
"lasso_feat_imp = pd.DataFrame({\n",
" 'Embedding': FEATURES,\n",
" 'Coef_mean': lasso_coef_mean,\n",
" 'Coef_std': lasso_coef_std\n",
"}).sort_values('Coef_mean', key=abs, ascending=False)\n",
"\n",
"# === Display results ===\n",
"print(f\"Average R² across folds: {lasso_r2_mean:.3f}\")\n",
"\n",
"plt.figure(figsize=(10,5))\n",
"plt.bar(range(10), np.abs(lasso_feat_imp['Coef_mean'].iloc[:10]),\n",
" yerr=lasso_feat_imp['Coef_std'].iloc[:10],\n",
" color='royalblue', alpha=0.7)\n",
"plt.xticks(range(10), lasso_feat_imp['Embedding'].iloc[:10], rotation=45)\n",
"plt.title(f'Top 10 Embedding Features Predicting {TARGET_INDEX} (Lasso)')\n",
"plt.ylabel('|Mean Coefficient|')\n",
"plt.grid(alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
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"execution_count": null,
"id": "adea5d1a-3019-4fcd-8842-534eb2260ebf",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 601
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"executionInfo": {
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"status": "ok",
"timestamp": 1759957294099,
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"source": [
"# --- Combine RF importance and Lasso coefficients ---\n",
"merged = rf_feat_imp.merge(lasso_feat_imp, on='Embedding', how='inner')\n",
"\n",
"plt.figure(figsize=(7,6))\n",
"plt.scatter(\n",
" merged['Importance_mean'],\n",
" np.abs(merged['Coef_mean']),\n",
" alpha=0.7,\n",
" color='purple'\n",
")\n",
"\n",
"# Highlight top RF and Lasso features\n",
"top_features = ['A44', 'A60']\n",
"for feat in top_features:\n",
" row = merged[merged['Embedding'] == feat]\n",
" x_val = row['Importance_mean'].iloc[0] # convert Series to float\n",
" y_val = np.abs(row['Coef_mean'].iloc[0]) # convert Series to float\n",
" plt.scatter(x_val, y_val, color='red', s=100)\n",
" plt.text(x_val + 0.005, y_val + 0.001, feat, fontsize=10)\n",
"\n",
"plt.xlabel('Random Forest Mean Importance')\n",
"plt.ylabel('|Lasso Mean Coefficient|')\n",
"plt.title('Embedding Feature Influence on NDVI (RF vs Lasso)')\n",
"plt.grid(alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
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"source": [
"# End"
]
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