Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning
arXiv:2606.14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery. Traditional methods, which often rely on manual inspections or sparse data, are typically slow and error-prone. This paper introduces a novel framework leveraging remote sensing imagery and deep learning to automate building damage classification. Using pre- and post-disaster satellite imagery, our model categorizes buildings into four damage levels: no damage, minor damage, major damage, and destroyed. The core innovat
The increasing availability of high-resolution remote sensing data combined with advancements in deep learning models makes this type of automated assessment technically feasible and highly relevant.
Accurate and rapid disaster damage assessment is critical for optimizing humanitarian aid, insurance claims processing, and rebuilding efforts, significantly improving resilience to natural catastrophes.
Traditional manual and slow damage assessment processes can now be significantly augmented or replaced by automated, AI-driven systems, leading to faster and more efficient disaster response.
- · Emergency response organizations
- · Insurance companies
- · Satellite imagery providers
- · AI/deep learning developers
- · Manual damage assessment services
- · Inefficient humanitarian aid logistics
Faster and more targeted deployment of resources post-disaster, enabled by rapid damage mapping.
Reduced economic losses from disasters due to quicker recovery, impacting insurance premiums and reconstruction costs.
Enhanced urban planning and infrastructure development, incorporating resilience features based on more granular damage data and predictive models.
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Read at arXiv cs.AI