Building Change Detection in Earthquake: A Multi-Scale Interaction Network and A Change Detection Dataset
arXiv:2606.10329v1 Announce Type: cross Abstract: As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses. Change detection (CD) can be applied to post-earthquake damage assessment as it can infer destroyed change regions from multi-temporal remote sensing images. Furthermore, the CD with short imaging interval will better satisfy the needs of the emergency rescues after earthquakes. However, the capability of current methods built on deep neural networks is limited because the dataset with short
The increasing frequency of severe natural disasters like earthquakes and advancements in AI for image analysis drive the need for rapid damage assessment solutions.
This development improves the speed and accuracy of post-disaster response, enabling more effective allocation of resources and potentially saving lives by identifying damaged regions with greater precision.
The capability to perform rapid and automated damage assessment using multi-temporal satellite imagery and AI is enhanced, moving beyond manual or less efficient methods.
- · Emergency response organizations
- · Satellite imagery providers
- · AI/computer vision developers
- · Governments
- · Manual damage assessment methods
- · Regions without access to satellite infrastructure
Faster and more targeted aid allocation in earthquake-affected areas.
Reduced economic losses from disasters due to quicker identification of damage and subsequent recovery efforts.
Integration of similar AI-driven change detection into other disaster response scenarios, such as floods or wildfires.
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Read at arXiv cs.AI