Dataset descriptions updated

README.md

@@ -8,6 +8,40 @@ This dataset card contains usage instructions and metadata for all data-products
 | [BigEarthNet v2.0](https://bigearth.net/static/documents/Description_BigEarthNet_v2.pdf) | Land-cover classification | Sentinel-2 (10 bands) | ViT | [SatCLIP](https://arxiv.org/abs/2311.17179) embeddings | ✓ |
 | [USAVars](https://arxiv.org/abs/2010.08168) | Tree-cover regression | NAIP RGB + NIR | ResNet-50 | OSM rasters, DEM | ✗ |
 
+## 📦 Datasets & Georeferenced Auxiliary Layers
+
+### SustainBench – Farmland Boundary Delineation  
+* **Optical input:** Sentinel-2 RGB patches (224 × 224 px, 10 m GSD) covering French cropland in 2017; ≈ 1.6 k training images.  
+* **Auxiliary layers (all geo-aligned):**  
+  * 19-channel OpenStreetMap (OSM) raster stack (roads, waterways, buildings, biome classes, …)  
+  * EU-DEM (20 m GSD, down-sampled to 10 m)  
+* **Why:** OSM + DEM give an 8 % Dice boost when labels are scarce; gains appear once the training set drops below ≈ 700 images.   
+
+---
+
+### EnviroAtlas – Land-Cover Segmentation  
+* **Optical input:** NAIP 4-band RGB-NIR aerial imagery at 1 m resolution.  
+* **Auxiliary layers:**  
+  * OSM rasters (roads, waterbodies, waterways)  
+  * **Prior** raster – a hand-crafted fusion of NLCD land-cover and OSM layers (PROC-STACK)  
+* **Splits:** Train = Pittsburgh; OOD validation/test = Austin & Durham. Auxiliary layers raise OOD overall accuracy by ~4 pp without extra fine-tuning.   
+
+---
+
+### BigEarthNet v2.0 – Multi-Label Land-Cover Classification  
+* **Optical input:** 10-band Sentinel-2 tile pairs; ≈ 550 k patch/label pairs over 19 classes.  
+* **Auxiliary layer:**  
+  * **SatCLIP location embedding** (256-D), one per image centre, injected as an extra ViT token (TOKEN-FUSE).  
+* **Splits:** Grid-based; val/test tiles lie outside the training footprint (spatial OOD by design). SatCLIP token lifts macro-F1 by ~3 pp across *all* subset sizes.   
+
+---
+
+### USAVars – Tree-Cover Regression  
+* **Optical input:** NAIP RGB-NIR images (1 km² tiles); ≈ 100 k samples with tree-cover % labels.  
+* **Auxiliary layers:**  
+  * Extended OSM raster stack (roads, buildings, land-use, biome classes, …)  
+  * Continental Europe Digital Elevation Model (DEM) resampled to 10 m GSD  
+* **Notes:** Stacking the OSM raster boosts R² by 0.16 in the low-data regime (< 250 images); DEM is provided raw for flexibility.
 
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 license: mit