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Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

Sun, X.; Wang, P.; Yan, Z.; Diao, W.; Lu, X.; Yang, Z.; Zhang, Y.; Xiang, D.; Yan, C.; Guo, J.; Dang, B.; Wei, W.; Xu, F.; Wang, C.; Hansch, R.; Weinmann, M. 1; Yokoya, N.; Fu, K.
1 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)

Abstract:

In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge.


Verlagsausgabe §
DOI: 10.5445/IR/1000137779
Veröffentlicht am 23.09.2021
Originalveröffentlichung
DOI: 10.1109/JSTARS.2021.3106941
Scopus
Zitationen: 11
Dimensions
Zitationen: 14
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1939-1404, 2151-1535
KITopen-ID: 1000137779
Erschienen in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Band 14
Nachgewiesen in Dimensions
Scopus
Web of Science
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