5 papers
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…
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…
Aligning load flexibility with emissions reduction: empirical insights from a multi-site study of cryptocurrency data centers
Veronica M. Paez, Neda Mohammadi, John E. Taylor
The power sector is responsible for 32 percent of global greenhouse gas emissions. Data centers and cryptocurrencies use significant amounts of electricity and contribute to these…
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…
Short-term Streamflow and Flood Forecasting based on Graph Convolutional Recurrent Neural Network and Residual Error Learning
Xiyu Pan, Neda Mohammadi, John E. Taylor
Accurate short-term streamflow and flood forecasting are critical for mitigating river flood impacts, especially given the increasing climate variability. Machine learning-based st…