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cs.CV2025
A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery
Thomas Manzini, Priyankari Perali, Raisa Karnik +1
This paper presents the largest known benchmark dataset for road damage assessment and road alignment, and provides 18 baseline models trained on the CRASAR-U-DRIODs dataset's post…
cs.CV2025
Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response
Thomas Manzini, Priyankari Perali, Robin R. Murphy
This paper presents the first AI/ML system for automating building damage assessment in uncrewed aerial systems (sUAS) imagery to be deployed operationally during federally declare…
cs.CV2025
Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery
Thomas Manzini, Priyankari Perali, Jayesh Tripathi +1
This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15,814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.02% label dis…