arXiv:2606.17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery. While most deep learning methods rely on spatial-domain features, frequency-domain representations can capture complementary structural cues such as debris patterns and collapse-induced textures. This study presents a controlled comparison of spatial-domain, frequency-domain, and dual-domain deep learning approaches for multi-class building damage classification using post-disaster imagery from the xView2 (xBD) dataset. To ensur
The increasing availability of high-resolution satellite imagery and advancements in deep learning techniques are enabling more sophisticated disaster assessment methods.
Improved rapid and precise disaster assessment using AI can significantly accelerate humanitarian response, allocate resources more effectively, and reduce recovery costs.
This research highlights a new methodology for damage assessment, moving beyond purely spatial analysis to integrate frequency-domain representations, potentially improving accuracy and robustness.
- · Disaster response agencies
- · Satellite imagery providers
- · AI/ML researchers in remote sensing
- · Insurance companies
- · Traditional manual assessment methods
- · Regions without access to advanced satellite data
More accurate and faster damage assessments will streamline initial humanitarian aid deployment.
Enhanced data could inform better urban planning and infrastructure resilience strategies in disaster-prone areas.
The integration of diverse data modalities in AI for critical infrastructure could become a standard across multiple sectors, leading to a demand for multi-modal AI expertise.
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Read at arXiv cs.AI