Zhuohong Li, Fangxiao Lu, Hongyan Zhang, Lilin Tu, Jiayi Li, Xin Huang, Caleb Robinson, Kolya Malkin, Nebojsa Jojic, Pedram Ghamisi, Ronny Hansch, and Naoto Yokoya

Example predictions.

Example predictions from the baseline models.

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.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 2022-01-24.

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IARAI Authors
Dr. Pedram Ghamisi
Remote Sensing
Convolutional Neural Network, Data Fusion, Deep Learning, Image Classification, Land Cover, Random Forest, Weak Supervision


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