AI RESEARCH
From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery
arXiv CS.CV
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ArXi:2603.22768v1 Announce Type: new Rapid and accurate structural damage assessment following natural disasters is critical for effective emergency response and recovery. However, remote sensing imagery often suffers from low spatial resolution, contextual ambiguity, and limited semantic interpretability, reducing the reliability of traditional detection pipelines. In this work, we propose a novel hybrid framework that integrates AI-based super-resolution, deep learning object detection, and Vision-Language Models (VLMs) for comprehensive post-disaster building damage assessment.