HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment
When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after imagery and a training set drawn from similar past events, and neither is usually available for a new disaster in its first day. We present HASTE (High-speed Assessment and Satellite Tracking for Emergencies), a no-code web platform that lets analysts who are not machine learning engineers produce per-building damage maps from post-disaster satellite imagery. HASTE implements two methods that share one interface. The firs
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- PossiblePossibly related (embedding) · 47%Cross-scenario evaluation of explainable machine learning for non-invasive summer occupancy detection across five building scenarios - Nature →
- PossiblePossibly related (embedding) · 45%AI Improves Earthquake Detection - eos.org →
- LinkedLinked via arxiv author · 85%Caleb Robinson →
“HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment”
- LinkedLinked via arxiv author · 85%Anthony Ortiz →
“HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment”
- LinkedLinked via arxiv author · 85%Simone Fobi Nsutezo →
“HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment”
- LinkedLinked via arxiv author · 85%Cameron Birge →
“HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment”
- LinkedLinked via arxiv author · 85%Meygha Machado →
“HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment”
- LinkedLinked via arxiv author · 85%Marcelo Duarte →
“HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment”