TY - GEN
T1 - OpenEarthMap
T2 - 23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023
AU - Xia, Junshi
AU - Yokoya, Naoto
AU - Adriano, Bruno
AU - Broni-Bediako, Clifford
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarth-Map 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. Se-mantic segmentation models trained on the OpenEarth-Map 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.
AB - We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarth-Map 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. Se-mantic segmentation models trained on the OpenEarth-Map 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.
KW - Applications: Remote Sensing
KW - Image recognition and understanding (object detection, categorization, segmentation, scene modeling, visual reasoning)
UR - http://www.scopus.com/inward/record.url?scp=85145567429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145567429&partnerID=8YFLogxK
U2 - 10.1109/WACV56688.2023.00619
DO - 10.1109/WACV56688.2023.00619
M3 - Conference contribution
AN - SCOPUS:85145567429
T3 - Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
SP - 6243
EP - 6253
BT - Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 January 2023 through 7 January 2023
ER -