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--- |
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pretty_name: Geolayers |
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language: en |
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language_creators: |
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- found |
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license: cc-by-4.0 |
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multilinguality: monolingual |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- image-classification |
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- image-segmentation |
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source_datasets: |
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- SustainBench |
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- USAVars |
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- BigEarthNetv2.0 |
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- EnviroAtlas |
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homepage: https://huggingface.co/datasets/arjunrao2000/geolayers |
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repository: https://huggingface.co/datasets/arjunrao2000/geolayers |
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download_size: 25570000000 |
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tags: |
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- climate |
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- remote-sensing |
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data_files: |
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- "huggingface_preview/**/*.jpg" |
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- "metadata.csv" |
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preview: |
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path: metadata.csv |
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images: |
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- rgb |
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- osm |
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- dem |
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- mask |
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configs: |
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- config_name: benchmark |
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data_files: |
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- split: sustainbench |
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path: metadata.csv |
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--- |
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# Geolayers-Data |
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<img src="osm_usavars.png" alt="Sample Geographic Inputs with the USAVars Dataset" width="800"/> |
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--> |
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This dataset card contains usage instructions and metadata for all data-products released with our paper: |
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*Using Multiple Input Modalities can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery.* We release 3 modified versions of 3 benchmark datasets spanning land-cover segmentation, tree-cover regression, and multi-label land-cover classification tasks. These datasets are augmented with auxiliary, geographic inputs. A full list of contributed data products is shown in the table below. |
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<table> |
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<thead> |
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<tr> |
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<th>Dataset</th> |
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<th>Task Description</th> |
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<th>Multispectral Input</th> |
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<th>Model</th> |
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<th>Additional Data Layers</th> |
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<th colspan="2">Dataset Size</th> |
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<th>OOD Test Set Present?</th> |
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</tr> |
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<tr> |
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<th></th> |
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<th></th> |
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<th></th> |
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<th></th> |
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<th></th> |
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<th>Compressed</th> |
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<th>Uncompressed</th> |
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<th></th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td><a href="https://arxiv.org/abs/2111.04724">SustainBench</a></td> |
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<td>Farmland boundary delineation</td> |
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<td>Sentinel-2 RGB</td> |
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<td>U-Net</td> |
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<td>OSM rasters, EU-DEM</td> |
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<td>1.76 GB</td> |
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<td>1.78 GB</td> |
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<td>✗</td> |
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</tr> |
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<tr> |
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<td><a href="https://arxiv.org/abs/2202.14000">EnviroAtlas</a></td> |
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<td>Land-cover segmentation</td> |
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<td>NAIP RGB + NIR</td> |
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<td>FCN</td> |
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<td><a href="https://arxiv.org/abs/2202.14000">Prior</a>, OSM rasters</td> |
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<td>N/A</td> |
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<td>N/A</td> |
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<td>✓</td> |
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</tr> |
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<tr> |
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<td><a href="https://bigearth.net/static/documents/Description_BigEarthNet_v2.pdf">BigEarthNet v2.0</a></td> |
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<td>Land-cover classification</td> |
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<td>Sentinel-2 (10 bands)</td> |
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<td>ViT</td> |
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<td><a href="https://arxiv.org/abs/2311.17179">SatCLIP</a> embeddings</td> |
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<td>120 GB (raw), 91 GB (H5)</td> |
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<td>205 GB (raw), 259 GB (H5) </td> |
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<td>✓</td> |
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</tr> |
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<tr> |
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<td><a href="https://arxiv.org/abs/2010.08168">USAVars</a></td> |
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<td>Tree-cover regression</td> |
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<td>NAIP RGB + NIR</td> |
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<td>ResNet-50</td> |
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<td>OSM rasters</td> |
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<td> 23.56 GB </td> |
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<td> 167 GB</td> |
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<td>✗</td> |
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</tr> |
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</tbody> |
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</table> |
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## Usage Instructions |
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* 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) |
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* 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>` |
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* Datasets with auxiliary geographic inputs can be read with H5PY. |
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### Usage Instructions for the BigEarthNetv2.0 dataset (Clasen et. al. (2025)) |
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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) |
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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: |
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1. We download the raw `BigEarthNet-S2.tar.zst` Sentinel-2 BigEarthNet dataset. |
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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. |
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3. We download reference maps and sentinel-2 tile metadata with snow and cloud cover rasters |
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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. |
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To uncompress and re-assemble the compressed files in `data/bigearthnet/raw/`, download all the parts and run: |
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``` |
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cat bigearthnet.tar.gz.part-* \ |
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| pigz -dc \ |
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| tar -xpf - |
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``` |
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Note that if this version of the dataset is used, SatCLIP embeddings would need to be re-computed on-the-fly. To use this dataset with the pre-computed SatCLIP embeddings, refer to the note below. |
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#### 💡 Do you want to try your own input fusion mechanism with BigEarthNetv2.0? |
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The second version of the BigEarthNetv2.0 dataset is stored in `data/bigearthnet/`. These datasets are stored as 3 H5PY datasets (`.h5`) for each split in the dataset. |
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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). |
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You may access these embeddings and location metadata with keys `location` and `satclip_embedding`. |
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### Usage Instructions for the SustainBench Farmland Boundary Delineation Dataset (Yeh et. al. (2021)) |
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1. Unzip the archive in `data/sustainbench-field-boundary-delineation` with `unzip sustainbench.zip` |
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2. You should see a directory structure as follows: |
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``` |
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dataset_release/ |
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├── id_augmented_test_split_with_osm_new.h5.gz.zip |
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├── id_augmented_train_split_with_osm_new.h5.gz.zip |
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├── id_augmented_val_split_with_osm_new.h5.gz.zip |
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├── raw_id_augmented_test_split_with_osm_new.h5.gz.zip |
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├── raw_id_augmented_train_split_with_osm_new.h5.gz.zip |
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└── raw_id_augmented_val_split_with_osm_new.h5.gz.zip |
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``` |
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3. Unzip all files using `unzip` and `pigz -d <path to .h5.gz file>` |
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There are two versions of data released: Datasets that begin with `id_augmented` refer to the version of the SustainBench farmland boundary delineation dataset with the OSM and DEM rasters pre-processed to RGB space following the application of the Gaussian Blur. Datasets that begin with `raw_id_augmented` contain the RGB imagery with 19 categorical rasters for OSM, and 1 raster for the DEM geographic input. |
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## 📦 <a name="geolayersused"></a> Datasets & Georeferenced Auxiliary Layers |
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### SustainBench – Farmland Boundary Delineation |
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* **Optical input:** Sentinel-2 RGB patches (224×224 px, 10 m GSD) covering French cropland in 2017; ≈ 1.6 k training images. |
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* **Auxiliary layers (all geo-aligned):** |
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* 19-channel OpenStreetMap (OSM) raster stack (roads, waterways, buildings, biome classes, …) |
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* EU-DEM (20 m GSD, down-sampled to 10 m) |
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* **Why:** OSM + DEM give an 8 % Dice boost when labels are scarce; gains appear once the training set drops below ≈ 700 images. |
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--- |
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### EnviroAtlas – Land-Cover Segmentation |
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* **Optical input:** NAIP 4-band RGB-NIR aerial imagery at 1 m resolution. |
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* **Auxiliary layers:** |
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* OSM rasters (roads, waterbodies, waterways) |
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* **Prior** raster – a hand-crafted fusion of NLCD land-cover and OSM layers (PROC-STACK) |
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* **Splits:** Train = Pittsburgh; OOD validation/test = Austin & Durham. Auxiliary layers raise OOD overall accuracy by ~4 pp without extra fine-tuning. |
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--- |
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### BigEarthNet v2.0 – Multi-Label Land-Cover Classification |
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* **Optical input:** 10-band Sentinel-2 tile pairs; ≈ 550 k patch/label pairs over 19 classes. |
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* **Auxiliary layer:** |
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* **SatCLIP** location embedding (256-D), one per image center, injected as an extra ViT token (TOKEN-FUSE). |
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* **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. |
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--- |
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### USAVars – Tree-Cover Regression |
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* **Optical input:** NAIP RGB-NIR images (1 km² tiles); ≈ 100 k samples with tree-cover % labels. |
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* **Auxiliary layers:** |
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* Extended OSM raster stack (roads, buildings, land-use, biome classes, …) |
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* **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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Citation: |
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``` |
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@inproceedings{ |
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rao2025using, |
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title={Using Multiple Input Modalities can Improve Data-Efficiency and O.O.D. Generalization for {ML} with Satellite Imagery}, |
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author={Arjun Rao and Esther Rolf}, |
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booktitle={TerraBytes - ICML 2025 workshop}, |
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year={2025}, |
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url={https://openreview.net/forum?id=p5nSQMPUyo} |
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} |
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``` |
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--- |
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