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

• URL: http://arxiv.org/abs/2210.10732v1
• Date: Wed, 19 Oct 2022 17:20:16 GMT
• Title: OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
• Authors: Junshi Xia, Naoto Yokoya, Bruno Adriano, Clifford Broni-Bediako
• Abstract summary: OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. It consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents.
• Score: 15.419052489797775
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance. Semantic segmentation models trained on the OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications. We evaluate the performance of state-of-the-art methods for unsupervised domain adaptation and present challenging problem settings suitable for further technical development. We also investigate lightweight models using automated neural architecture search for limited computational resources and fast mapping. The dataset is available at https://open-earth-map.org.

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