4th Multi-modal Aerial View Image Challenge: SAR Classification - PBVS 2025

Nathan Inkawhich, Claire Thorp, Justice Wheelwright, Oliver Nina, Dylan Bowald, Angel Sappa, Erik Blasch; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2025, pp. 4670-4678

The Multi-modal Aerial View Image Challenge - Classification Track (MAVIC-C) continues to push the boundaries of multi-modal object recognition by encouraging researchers to innovate models that leverage both Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery. This paper analyzes the outcomes of the new iteration of this challenge and emphasizes the critical role of EO and SAR data fusion in remote sensing tasks. This year MAVIC-C saw impressive developments of sophisticated multi-modal approaches that address the distinct properties and challenges inherent to the data. This year's challenge notably builds on insights from previous iterates: in 2021 we demonstrated the potential of EO and SAR integration; in 2022 and 2023 we explored the capabilities of multi-modal frameworks; and in 2024 we examined model robustness in out-of-distribution scenarios. This year, we started with the same challenge design as 2024 and asked teams to further advance techniques for improving accuracy and of out-of-distribution detection, which builds model robustness. Overall, this manuscript provides an in-depth investigation of the methodologies of top-performing teams and analyzes participant's performance on a sequestered test set.