7 papers
Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones
Sanjay Thasma, Yu-Hsuan Ho, Ali Mostafavi
Rapid flood mapping is critical for emergency response, yet optical imagery is often unusable during major flooding and single-temporal SAR is ambiguous, since new inundation, perm…
Damage-TriageFormer: A Foundation-Model Framework for Typology-Based Building Damage Assessment from Mono-Temporal Imagery
Yiming Xiao, Yu-Hsuan Ho, Sanjay Thasma +2
Decision-relevant building damage assessment is critical for prioritizing resources and recovery after a disaster, yet most automated methods either flatten damage into a single se…
Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation models
Lipai Huang, Adithi Srinath, Manas Singh +2
Near-real-time flood depth prediction demands surrogate models that are accurate, fast, and transferable across watersheds. Supervised surrogates can match physics-based simulators…
Property-Level Flood Risk Assessment Using AI-Enabled Street-View Lowest Floor Elevation Extraction and ML Imputation Across Texas
Xiangpeng Li, Yu-Hsuan Ho, Sam D Brody +1
This paper argues that AI-enabled analysis of street-view imagery, complemented by performance-gated machine-learning imputation, provides a viable pathway for generating building-…
Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery
Yiming Xiao, Archit Gupta, Miguel Esparza +5
Building-level occupancy after disasters is vital for triage, inspections, utility re-energization, and equitable resource allocation. Overhead imagery provides rapid coverage but…
Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery
Yu-Hsuan Ho, Ali Mostafavi
Most post-disaster damage classifiers succeed only when destructive forces leave clear spectral or structural signatures -- conditions rarely present after inundation. Consequently…