20 citations · 24 across the 7 of their papers we have counts for
10 papers
FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks
Aiyshwariya Paulvannan Kanmani, Miguel Esparza, Ali Mostafavi
Wildfire decisions are made in hours, yet most learned spread models predict daily on targets including burned area. We propose FireGraphNet which formulates 12-hour growth as node…
Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction
Miguel Esparza, Aydin Ayanzadeh, Aydin Ayanzadeh Ahmad Mousavi +2
Next-day wildfire prediction requires models whose forecasts can be evaluated alongside the assumptions and historical evidence used in their computation. Although deep learning ca…
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…
High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data
Lipai Huang, Federico Antolini, Ali Mostafavi +3
High-resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated dat…
ELEV-VISION-SAM: Integrated Vision Language and Foundation Model for Automated Estimation of Building Lowest Floor Elevation
Yu-Hsuan Ho, Longxiang Li, Ali Mostafavi
Street view imagery, aided by advancements in image quality and accessibility, has emerged as a valuable resource for urban analytics research. Recent studies have explored its pot…