Update README.md

README.md

@@ -61,7 +61,25 @@ 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                                        | ✗                         |
 
-## 📦 Datasets & Georeferenced Auxiliary Layers
+## Usage Instructions
+* Download the `.h5.gz` files in `data/<source dataset name>`. Our source datasets include SustainBench, USAVars, and BigEarthNet2.0. Each dataset with the augmented geographic inputs is detailed in [this section 📦](#geolayersused) 
+* You may use pigz (https://linux.die.net/man/1/pigz) to decompress the archive. This is especially recommended for USAVars' train-split, which is 117 GB when uncompressed. This can be done with `pigz -d <.h5.gz>`
+* Datasets with auxiliary geographic inputs can be read with H5PY.
+
+### Usage Instructions for the BigEarthNetv2.0 dataset (Clasen et. al. (2025))
+We use the original dataset [BigEarthNetv2.0](https://bigearth.net/) dataset which is processed with spatially-buffered train-test splits. We release two **processed** versions of the datasets introduced in Casen et. al. (2025)
+The first version is stored in directory `data/bigearthnet/raw/` This dataset, although called `raw` is a pre-processed version of the raw BigEarthNetv2.0 dataset. We follow instructions listed on [this repository](https://git.tu-berlin.de/rsim/reben-training-scripts/-/tree/main?ref_type=heads#data). Steps performed:
+  1. We download the raw `BigEarthNet-S2.tar.zst` Sentinel-2 BigEarthNet dataset.
+  2. We extract and process the raw S2 tiles to a LMDB 'Lightning' Database. This allows for faster reads during training. We use the rico-hdl tool [here](https://github.com/kai-tub/rico-hdl) to accomplish this.
+  3. We download reference maps and sentinel-2 tile metadata with snow and cloud cover rasters
+  4. This final dataset is compressed into several chunks and stored in `data/bigearthnet/raw/bigearth.tar.gz.part-a<x>`. Each chunk is 5G large. There are 24 total chunks.
+
+### 💡 Do you want to try your own input fusion mechanism with BigEarthNetv2.0? 
+The second version of the BigEarthNetv2.0 dataset is stored in `data/bigearthnet/processed/`. These datasets are stored as 3 H5PY datasets (`.h5`) for each split in the dataset. 
+This version of the processed dataset comes with (i) raw location co-ordinates, and (ii) pre-computed SatCLIP embeddings (L=10, ResNet50 image encoder backbone). 
+You may access these embeddings and location metadata with keys `location` and 'satclip_embedding'. 
+
+## 📦 <a name="geolayersused"></a> 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.  
@@ -95,10 +113,7 @@ This dataset card contains usage instructions and metadata for all data-products
   * Extended OSM raster stack (roads, buildings, land-use, biome classes, …)  
 * **Notes:** Stacking the OSM raster boosts R² by 0.16 in the low-data regime (< 250 images); DEM is provided raw for flexibility.
 
-## Usage Instructions
-* Download the `.h5.gz` files in `data/<source dataset name>`. Our source datasets include SustainBench, USAVars, and BigEarthNet2.0
-* You may use pigz (https://linux.die.net/man/1/pigz) to decompress the archive. This is especially recommended for USAVars' train-split, which is 117 GB when uncompressed. This can be done with `pigz -d <.h5.gz>`
-* Datasets with auxiliary geographic inputs can be read with H5PY.
+
 
 Citation: