Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 15, Issue -, Pages 1643-1655Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2022.3144318
Keywords
Remote sensing; Earth; Data integration; Vegetation mapping; Predictive models; Vegetation; Artificial satellites; Convolutional neural networks; deep learning; image analysis and data fusion; land cover change detection; multimodal; random forests; weak supervision
Categories
Funding
- National Natural Science Foundation of China [41971295]
- CAS Interdisciplinary Innovation Team [JCTD-2019-04]
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This article presents the scientific outcomes of the 2021 Data Fusion Contest, focusing on the multitemporal semantic change detection task. It provides an overview of the DFC2021 dataset and reports the results of the best-performing methods during the contest.
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.
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