All-Weather Land Cover and Building Damage Mapping: Outcome of the 2025 IEEE GRSS Data Fusion Contest

This article presents the scientific results of the 2025 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee, the University of Tokyo, RIKEN, and ETH Zurich. The focus of the contest was to develop innovative solutions for all-weather land-cover and building damage mapping using multimodal SAR and optical EO data at submeter resolution. The contest is organized into two distinct tracks. Track 1 focuses on land-cover mapping and Track 2 is about building damage mapping. The competition presented two primary technical challenges: the effective integration of multimodal data and the development of robust models capable of handling noisy labels. The contest saw significant global engagement, with Track 1 receiving 507 registrations and 3,859 successful submissions, while Track 2 followed a similar trend with 423 registered teams and 5,008 successful entries. This paper provides the methodologies and results achieved by the first- and secondranked teams from each track. To promote transparency and ensure the reproducibility of results, all participating teams in this year's contest have publicly released their code. Furthermore, the dataset used in this competition has been made publicly available to the community to support and encourage further research in the field.