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README.md

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-license: mit
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+---
+license: mit
+---
+
+# FMARS: Foundation Model Annotations for Remote Sensing Images
+
+
+FMARS is a large-scale dataset of Very High Resolution (VHR) remote sensing images with annotations generated using Vision Foundation Models.
+The dataset focuses on disaster management applications and provides pre-event imagery and annotations for major crisis events worldwide from 2021 to 2023.
+
+## Dataset Features
+- **VHR Imagery**: The dataset uses pre-event VHR satellite imagery from the [Maxar Open Data Program](https://www.maxar.com/open-data), covering a total surface area of over 200,000 km^2.
+- **Automatic Annotations**: Annotations are generated using a novel pipeline that combines the Segment Anything Model (SAM) and GroundingDINO to extract segmentation masks for buildings, roads, and high vegetation.
+- **Disaster Management Focus**: The dataset is designed for use in disaster management applications such as damage assessment and risk analysis.
+
+## Annotation Pipeline
+The annotation workflow uses a combination of open data sources and Vision Foundation Models:
+1. Building footprints and road graphs are obtained from Microsoft's Building Footprints and Road Detection datasets and converted into prompts.
+2. High vegetation bounding boxes are generated using GroundingDINO with text queries.
+3. The bounding box prompts are fed into SAM to extract fine-grained segmentation masks for each category.
+4. The resulting masks are stored to allow for both instance and semantic segmentation tasks.
+
+## Dataset Structure
+FMARS dataset provides annotations in parquet format.
+The corresponding VHR images can be obtained from the [Maxar Open Data Program](https://www.maxar.com/open-data) website.
+We keep tha naming convention of the original files, to facilitate the match between images and labels.
+
+For the full list of events and their details, please refer to the original paper, linked above.
+
+## Applications and Benchmarks
+FMARS represents a first attempt at large-scale mapping, but it can be used to train semantic segmentation models for disaster management tasks, with precautions.
+
+## License and Citation
+FMARS annotations are licensed under MIT.
+If you use this dataset or want to talk about it in your research, please cite the following paper:
+
+```bibtex
+@inproceedings{fmars2024,
+  title={FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models},
+  author={Arnaudo, Edoardo and Lungo Vaschetti, Jacopo and Innocenti, Lorenzo and Barco, Luca and Lisi, Davide and Fissore, Vanina and Rossi, Claudio},
+  booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},
+  year={2024},
+  organization={IEEE}
+}
+```