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OpenEarthMap Land Cover Mapping Few-Shot Challenge
The OpenEarthMap is a remote sensing (RS) image semantic segmentation benchmark dataset consisting of aerial/satellite images covering 97 regions from 44 countries across 6 continents at a spatial resolution of 0.25–0.5m ground sampling distance for global high-resolution land cover mapping. It presents an advancement in geographical diversity and annotation quality, enabling models to generalize worldwide. This challenge extends the original RS semantic segmentation task of the OpenEarthMap benchmark to generalized few-shot semantic segmentation (GFSS) task in RS image understanding.
The challenge aims to evaluate and benchmark learning methods for few-shot semantic segmentation on the OpenEarthMap dataset to promote research in AI for social good. The motivation is to enable researchers to develop few-shot learning algorithms for high-resolution RS image semantic segmentation, which is a fundamental problem in various applications of RS image understanding, such as disaster response, urban planning, and natural resource management.
The challenge is part of the 3rd Workshop on Learning with Limited Labelled Data for Image and Video Understanding (L3D-IVU) in conjunction with CVPR 2024. Scientific papers describing the best entries will be included and presented at the L3D-IVU CVPR 2024 Workshop, and published in the CVPR 2024 Workshops Proceedings.
Challenge deadline: 29th March, 2024
Organizers:
Clifford Broni-Bediako, RIKEN-AIP, Japan (clifford.broni-bediako@riken.jp)
Junshi Xia, RIKEN-AIP, Japan (junshi.xia@riken.jp)
Jian Song, The University of Tokyo, Japan (song@ms.k.u-tokyo.ac.jp)
Hongruixuan Chen, The University of Tokyo, Japan (qschrx@g.ecc.u-tokyo.ac.jp)
Naoto Yokaya, The University of Tokyo/RIKEN-AIP, Japan (yokoya@k.u-tokyo.ac.jp)
Prizes:
1st Winner: $1000
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