🤖 AI Summary
This work addresses the critical delay in post-disaster or conflict-zone building damage assessment caused by satellite data downlink and ground processing bottlenecks. The authors propose an on-orbit AI system that compresses pre-event high-resolution optical imagery into compact latent representations uploaded to the satellite, enabling onboard comparison with post-event imagery for damage localization and classification. The method introduces, for the first time, a spaceborne-oriented latent-space compression scheme coupled with a robust comparison mechanism leveraging siamese networks, cross-attention modules, and targeted data augmentation to maintain high accuracy under extreme compression ratios and image misalignment. Experimental results demonstrate that the approach drastically reduces downlinked data volume while preserving task performance, thereby significantly enhancing the timeliness of emergency response.
📝 Abstract
Rapid identification of damaged buildings after natural disasters or on war areas is crucial to support emergency response and prioritize interventions. Earth Observation constellations provide timely, large-scale coverage, but actionable information is often delayed by data downlink constraints, on-ground processing, and human interpretation. Reducing this latency is essential to improve decision-making responsiveness. In this work, we propose an original AI-based system that enables object-level building damage assessment (localization and damage classification) directly onboard satellites from pre-disaster and post-disaster highresolution optical imagery. Available pre-disaster images are encoded on ground into compact latent representations, transmitted to the satellite, and compared on-board with newly acquired post-event observations. Leveraging AI interpretation capabilities and increasing processing capabilities on-board satellites, the proposed design enables processing directly at the data source, reducing the amount of information to be downlinked while preserving task-relevant content and improving overall system responsivity. We explore the design space through a systematic benchmark of onboard-compatible variants, analyzing the impact of siamese processing, cross-attention, latent-space compression, and robustness-oriented data augmentation. Experiments on xBD dataset demonstrate reliable and robust damage assessment under misalignment, with minimal performance degradation under strong compression.