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---
pretty_name: Geolayers
language: en
language_creators:
- found
license: cc-by-4.0
multilinguality: monolingual
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- image-segmentation
citation: |
@inproceedings{rao2025,
title={Using Multiple Input Modalities can Improve Data‐Efficiency and O.O.D. Generalization for ML with Satellite Imagery},
author={Arjun Rao and Esther Rolf},
year={2025},
booktitle={Under Review},
}
source_datasets:
- SustainBench
- USAVars
- BigEarthNetv2.0
- EnviroAtlas
homepage: https://huggingface.co/datasets/arjunrao2000/geolayers
repository: https://huggingface.co/datasets/arjunrao2000/geolayers
download_size: 25570000000
tags:
- climate
- remote-sensing
# Make all JPEGs under huggingface_preview/ available
data_files:
- "huggingface_preview/**/*.jpg"
- "metadata.csv" # ← ensure HF knows your CSV is a file
# Tell HF to use YOUR metadata.csv as the preview‐links table
preview: metadata.csv
# (Optional) if you need a named config/split:
configs:
- config_name: benchmark
data_files:
- split: preview
path: metadata.csv
---
## Geolayers-Data
This dataset card contains usage instructions and metadata for all data-products released with our paper:
*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.
| **Dataset** | **Task Description** | **Multispectral Input** | **Model** | **Additional Data Layers** | **OOD Test Set Present?** |
|--------------------------------------|------------------------------------|-----------------------------|------------|-------------------------------------------------------|---------------------------|
| [SustainBench](https://arxiv.org/abs/2111.04724) | Farmland boundary delineation | Sentinel-2 RGB | U-Net | OSM rasters, EU-DEM | ✗ |
| [EnviroAtlas](https://arxiv.org/abs/2202.14000) | Land-cover segmentation | NAIP RGB + NIR | FCN | [Prior](https://arxiv.org/abs/2202.14000), OSM rasters | ✓ |
| [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 center, 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.
## 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.
--- |