OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping

Project Page

https://open-earth-map.org/

Paper

https://arxiv.org/abs/2210.10732

Overview

OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.

Reference

@inproceedings{xia_2023_openearthmap,
    title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping},
    author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month = {January},
    year = {2023},
    pages = {6254-6264}
}

License

Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data here. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Note for xBD data

The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from https://xview2.org/dataset and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.

Code

Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available here.

Leaderboard

Performance on the test set can be evaluated on the Codalab webpage.