Upload README.md

README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+---
+
+## Summary
+We introduce the Geospatial Reasoning Segmentation Dataset (GRES), a collection of vision and language data designed around remote-sensing applications. GRES consists of two core components: PreGRES, a dataset consisting of over 1M remote-sensing specific visual instruction-tuning Q/A pairs for pre-training geospatial models, and GRES, a semi-synthetic dataset specialized for reasoning segmentation of remote-sensing data and consisting of 9,205 images and 27,615 natural language queries/answers within those images. From this LISAt dataset, we generate train, test, and validation splits consisting of 7,205, 1,500, and 500 images respectively.
+
+To generate synthetic data, we use the pipeline depicted below. We start with a seed detection dataset (xView). We then filter detections for those that are both visually interesting and highly distinguishable (A). For those detection, we then generate a natural language description (B), and a pixel-wise segmentation mask (C). Finally, the natural language description is used to generate a localization query (D). Together, the segmentation mask and the query form a ground-truth pair for the [LISAT](https://huggingface.co/jquenum/LISAt-7b) reasoning segmentation fine-tuning.
+
+<p align="center">
+  <img src="https://huggingface.co/datasets/jquenum/GRES/resolve/main/gres.png" width="1024"/>
+  
+</p>
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Define the dataset repo ID
+repo_id = "jquenum/GRES"
+
+# Download the dataset
+dataset = load_dataset(repo_id)
+
+# Show the dataset details
+print("Dataset loaded successfully!")
+print(dataset)
+
+# Access specific splits like train, validation, and test
+train_dataset = dataset['train']
+val_dataset = dataset['val']
+test_dataset = dataset['test']
+
+# Print the first example from the train split
+print("\nFirst example from the train dataset:", train_dataset[0])
+
+```
+
+run ```./extract_gres_images.sh /path/to/xview_train_images /path/to/xView_train.geojson .``` to get the gres image pool.
+
+
+## LISAT GRES Dataset
+
+This repository contains the LISAT GRES dataset, which includes image files and corresponding annotation files in JSON format. The dataset is organized into three main splits: **train**, **val**, and **test**.
+
+## Dataset Folder Structure
+
+This GRES dataset includes image files and corresponding annotation files in JSON format. The dataset is organized into three main splits: **train**, **val**, and **test**.
+
+- gres_images/
+  - train/
+    - lisat_gres_000000016192.jpg
+    - lisat_gres_000000016195.jpg
+    - lisat_gres_000000017340.jpg
+    - ...
+  - val/
+    - lisat_gres_000000016203.jpg
+    - lisat_gres_000000016210.jpg
+    - lisat_gres_000000017500.jpg
+    - ...
+  - test/
+    - lisat_gres_000000016217.jpg
+    - lisat_gres_000000016234.jpg
+    - lisat_gres_000000017800.jpg
+    - ...
+- gres_annotations/
+  - train/
+    - lisat_gres_000000016192.json
+    - lisat_gres_000000016195.json
+    - lisat_gres_000000017340.json
+    - ...
+    - train.txt
+  - val/
+    - lisat_gres_000000016203.json
+    - lisat_gres_000000016210.json
+    - lisat_gres_000000017500.json
+    - ...
+    - val.txt
+  - test/
+    - lisat_gres_000000016217.json
+    - lisat_gres_000000016234.json
+    - lisat_gres_000000017800.json
+    - ...
+    - test.txt
+    - large.txt
+    - small.txt
+
+
+## Citation
+
+If you use LISAt in your research or applications, please cite our paper:
+
+```bibtex
+@article{TBD,
+  title={LISAt: Language-Instructed Segmentation Assistant for Satellite Imagery},
+  author={Quenum, Jerome and Hsieh, Wen-Han and Wu, Tsung-Han and Gupta, Ritwik and Darrell, Trevor and Chan, David M},
+  journal={TBD},
+  year={2025},
+  url={TBD}
+}