Updated License and Metadata

README.md

@@ -1,18 +1,15 @@
 ---
-# ======= 1) Basic info =======
-pretty_name: "Geolayers"
+pretty_name: Geolayers
 language: en
 language_creators:
-  - "found"
-license: mit
+- found
+license: cc-by-4.0
 multilinguality: monolingual
 size_categories:
-  - 1K<n<100K
+- 10K<n<100K
 task_categories:
-  - image-classification
-  - image-segmentation
-
-# ======= 2) How to cite =======
+- 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},
@@ -20,42 +17,18 @@ citation: |
     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
+- SustainBench
+- USAVars
+- BigEarthNetv2.0
+- EnviroAtlas
+homepage: https://huggingface.co/datasets/arjunrao2000/geolayers
+repository: https://huggingface.co/datasets/arjunrao2000/geolayers
+download_size: 25570000000
 tags:
-  - climate
+- climate
+- remote-sensing
+preview: preview_inputs_sustainbench.csv
 ---
 
 
@@ -110,4 +83,4 @@ This dataset card contains usage instructions and metadata for all data-products
 * 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. 
----
+---