Showing cs.CVShow all
3 papers · 1 filter
cs.CV2025
FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph
Zhangding Liu, Neda Mohammadi, John E. Taylor
Timely and accurate floodwater depth estimation is critical for road accessibility and emergency response. While recent computer vision methods have enabled flood detection, they s…
cs.CV2025
MCANet: A Multi-Scale Class-Specific Attention Network for Multi-Label Post-Hurricane Damage Assessment using UAV Imagery
Zhangding Liu, Neda Mohammadi, John E. Taylor
Rapid and accurate post-hurricane damage assessment is vital for disaster response and recovery. Yet existing CNN-based methods struggle to capture multi-scale spatial features and…
cs.CV2025
Multi-Label Classification Framework for Hurricane Damage Assessment
Zhangding Liu, Neda Mohammadi, John E. Taylor
Hurricanes cause widespread destruction, resulting in diverse damage types and severities that require timely and accurate assessment for effective disaster response. While traditi…