Datasets:
ArXiv:
License:
| 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 | |
| ### 1. Download the [xView 1](https://xviewdataset.org/) dataset. | |
| ### 2. Clone this repository. | |
| ### 3. Run the command below: | |
| ```./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**. | |
| ```plaintext | |
| ├── 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 or GRES in your research or applications, please cite our [paper](https://arxiv.org/pdf/2505.02829): | |
| ```bibtex | |
| @article{quenum2025lisat, | |
| 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={arXiv preprint arXiv:2505.02829}, | |
| year={2025} | |
| } |