The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track DSE: Detection of Settlements Without Electricity

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Ronny Hansch¹  Jocelyn Chanussot²  Naoto Yokoya²  Pedram Ghamisi²  Yanbiao Ma³  Kexin Feng³  Qi Huang³  Hana Malha⁴  Colin Prieur⁴  Giorgio Licciardi⁴  Yu Xia⁵  Hongyan Zhang⁵  Yuxin Li⁵ 

[1] AI Strategy and Solutions, Hewlett Packard Enterprise Co., Grenoble, France;GIPSA-Lab, Grenoble INP, Saint Martin, France;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi&x2019;State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China;an, China;

关键词: Convolutional neural networks;    deep learning;    image analysis and data fusion;    land cover change detection;    multimodal;    random forests;   

DOI  :  10.1109/JSTARS.2021.3130446

来源: DOAJ

【 摘 要 】

In this article, we elaborate on the scientific outcomes of the 2021 Data Fusion Contest (DFC2021), which was organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society, on the subject of geospatial artificial intelligence for social good. The ultimate objective of the contest was to model the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data towards sustainable developments. DFC2021 consisted of two challenge tracks: Detection of settlements without electricity (DSE) and multitemporal semantic change detection. We focus here on the outcome of the DSE track. This article presents the corresponding approaches and reports the results of the best-performing methods during the contest.

【 授权许可】

Unknown