6 citations · 12 across the 6 of their papers we have counts for
6 papers
Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
Zhuoqun Xue, Xiaojian Zhang, David O. Prevatt +3
Accurately assessing building damage is critical for disaster response and recovery. However, many existing models for detecting building damage have poor prediction accuracy due t…
Social Vulnerabilities and Wildfire Evacuations: A Case Study of the 2019 Kincade Fire
Yuran Sun, Ana Forrister, Erica D. Kuligowski +3
Vulnerable populations are disproportionately impacted by natural hazards like wildfires. It is crucial to develop equitable and effective evacuation strategies to meet their uniqu…
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
Chenguang Wang, Yepeng Liu, Xiaojian Zhang +6
Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies pro…
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
Xiaojian Zhang, Xilei Zhao, Yiming Xu +2
Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. Howev…
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models
Yuran Sun, Shih-Kai Huang, Xilei Zhao
The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane prepared…
Spatial Equity of Micromobility Systems: A Comparison of Shared E-scooters and Station-based Bikeshare in Washington DC
Lin Su, Xiang Yan, Xilei Zhao
Many cities around the world have introduced dockless micromobility services in recent years and witnessed their rapid growth. Shared dockless e-scooters have the potential to bene…