First Update of metadata YAML

README.md

@@ -1,42 +1,105 @@
+---
+# ======= 1) Basic info =======
+pretty_name: "Geolayers"
+language: en
+language_creators:
+  - "found"
+license: mit
+multilinguality: monolingual
+size_categories:
+  - 1K<n<100K
+task_categories:
+  - image-classification
+  - image-segmentation
+
+# ======= 2) How to cite =======
+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},
+  }
+
+# ======= 3) Dataset structure =======
+source_datasets:
+  - "SustainBench"
+  - "USAVars"
+  - "BigEarthNetv2.0"
+  - "EnviroAtlas"
+
+# features:
+#   image:
+#     dtype: "uint8"
+#     shape: [3, 256, 256]
+#   osm_layers:
+#     dtype: "float32"
+#     shape: [4, 256, 256]
+#   label:
+#     ClassLabel:
+#       names: ["urban", "agriculture", "forest", "water", "bareground"]
+
+# splits:
+#   train:
+#     name: "train"
+#     num_examples: 8000
+#   validation:
+#     name: "validation"
+#     num_examples: 1000
+#   test:
+#     name: "test"
+#     num_examples: 1000
+
+# ======= 4) Other metadata =======
+homepage: "https://huggingface.co/datasets/arjunrao2000/geolayers"
+repository: "https://huggingface.co/datasets/arjunrao2000/geolayers"
+download_size: 2.557e+10
+tags:
+  - climate
+---
+
+
 ## 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 | ✗ |
+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.  
+### 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.   
+* **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  
+### 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.   
+* **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  
+### 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.   
+  * **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  
+### 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, …)  
@@ -44,10 +107,3 @@ This dataset card contains usage instructions and metadata for all data-products
 * **Notes:** Stacking the OSM raster boosts R² by 0.16 in the low-data regime (< 250 images); DEM is provided raw for flexibility.
 
 ---
-license: mit
-task_categories:
-- image-classification
-- image-segmentation
-tags:
-- climate
----