Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.3185-3199
• Main Authors: Robinson, Caleb, Malkin, Kolya, Jojic, Nebojsa, Chen, Huijun, Qin, Rongjun, Xiao, Changlin, Schmitt, Michael, Ghamisi, Pedram, Hansch, Ronny, Yokoya, Naoto
• Format: Article
• Language: English
• Subjects: Convolutional neural networks (CNNs); Data; Data integration; deep learning; Earth; High resolution; Image analysis; image analysis and data fusion; Image processing; Labels; Land cover; land-cover mapping; Mapping; multimodal; Multisensor fusion; random forests (RFs); Remote sensing; Resolution; Satellites; Synthetic aperture radar; Tensors; Training; weak supervision
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3063849

This article presents the scientific outcomes of the 2020 Data Fusion Contest (DFC2020) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2020 Contest addressed the problem of automatic global land-cover mapping with weak supervision, i.e., estimating high-resolution semantic maps while only low-resolution reference data are available during training. Two separate competitions were organized to assess two different scenarios: 1) high-resolution labels are not available at all; and 2) a small amount of high-resolution labels are available additionally to low-resolution reference data. In this article, we describe the DFC2020 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.