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| license: mit |
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| ## PreGRES: A Large-Scale Geospatial Dataset Collection |
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| **PreGRES** is a large-scale structured collection of existing smaller-scale geospatial datasets, designed for fine-tuning vision-language models in remote sensing applications. It integrates multiple sources, each contributing to different aspects of geospatial data understanding. |
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| The datasets within **PreGRES** support three major tasks, listed below. To use them, please download the associated image files via the provided links and place them in their respective folders. Then, download the **`pregres.json`** file and ensure your directory is organized as follows: |
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| ``` |
| ├── pregres.json |
| ├── NWPU-Captions |
| ├── RSICD |
| │ └── ... |
| ``` |
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| ### 1. Image Captioning |
| - [**NWPU-Captions**](https://github.com/HaiyanHuang98/NWPU-Captions) (Cheng et al., 2022) |
| - [**RSICD**](https://github.com/201528014227051/RSICD_optimal) (Lu et al., 2017) |
| - [**RSITMD**](https://github.com/xiaoyuan1996/AMFMN/blob/master/RSITMD/README.md) (Yuan et al., 2022b) |
| - [**Sydney-Captions**](https://pan.baidu.com/s/1hujEmcG#list/path=%2F) (Qu et al., 2016) |
| - [**UCM-Captions**](https://pan.baidu.com/s/1mjPToHq#list/path=%2F) (Qu et al., 2016) |
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| These datasets contribute paired image-text data and contain long-form descriptions of top-down imagery across diverse geospatial environments, enhancing language supervision. |
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| ### 2. Visual Question Answering (VQA) |
| - [**RSVQA LR** and **RSVQA HR**](https://rsvqa.sylvainlobry.com/#downloads) (Lobry et al., 2020) |
| - [**FloodNet**](https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021) (Rahnemoonfar et al., 2021) |
| - [**RSIVQA**](https://github.com/spectralpublic/RSIVQA) (Zheng et al., 2021) |
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| These datasets include structured question-answer pairs supporting reasoning over aerial and satellite images, covering tasks such as object identification, scene understanding, and disaster assessment. |
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| ### 3. Visual Grounding / Region-Level Captioning |
| - [**DIOR-RSVG**](https://drive.google.com/drive/folders/1hTqtYsC6B-m4ED2ewx5oKuYZV13EoJp_) (Zhan et al., 2023): Paired text-image data for object localization and spatial reference resolution. |
| - [**NWPU-RESISC45**](https://www.tensorflow.org/datasets/catalog/resisc45) (Cheng et al., 2017): Scene classification labels. |
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| ### Dataset Statistics |
| - **Images**: 119,279 |
| - **Question-Answer Pairs**: 1,204,993 |
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| PreGRES is used in the first-stage pre-training of the **LISAT** model, enabling general-purpose geospatial question answering. |
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| > For more details on dataset composition, see **Table C.9** in our [paper](https://arxiv.org/pdf/2505.02829). |
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| ## Citation |
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| If you use PreGRES or LISAT in your work, please cite: |
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| ```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} |
| } |
| |
