The Outcome of the 2021 IEEE GRSS Data Fusion Contest—Track MSD: Multitemporal Semantic Change Detection

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

Nebojsa Jojic¹  Naoto Yokoya²  Caleb Robinson³  Ronny Hansch⁴  Nikolay Malkin⁵  Lilin Tu⁶  Jiayi Li⁶  Xin Huang⁶  Zhuohong Li⁷  Hongyan Zhang⁷  Fangxiao Lu⁷  Pedram Ghamisi⁸ 

[1] de Montr&x00E9;Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Machine Learning Group, Freiberg, Germany;Microsoft AI for Good Research Lab, Redmond, USA;Microsoft Research, Redmond, WA, USA;Mila - Universit&x00E9;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China;al, Montreal, QC, Canada;

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

DOI  :  10.1109/JSTARS.2022.3144318

来源: DOAJ

【 摘 要 】

We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: “Detection of settlements without electricity” and “Multitemporal semantic change detection.” This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.

【 授权许可】

Unknown