2 citations · 6 across the 17 of their papers we have counts for
20 papers
DisasterLex: An Expert Concept-to-Schema Knowledge Graph for Geospatial Reasoning in Disaster Analytics
Yiming Xiao, Ankit Basu, Kai Yin +3
Disasters are inevitable and increasingly costly, and effective response depends on querying structured tabular data: precise, information-dense records of hazard, exposure, vulner…
DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints
Zhitong Chen, Kai Yin, Weifeng Zhang +7
Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage assessment, into coherent mu…
Automating UI Optimization through Multi-Agentic Reasoning
Zhipeng Li, Christoph Gebhardt, Yi-Chi Liao +1
We present AutoOptimization, a novel multi-objective optimization framework for adapting user interfaces. From a user's verbal preferences for changing a UI, our framework guides a…
Training-free retrieval-augmented generation with reinforced reasoning for flood damage nowcasting
Lipai Huang, Kai Yin, Chia-Fu Liu +1
We propose R2RAG-Flood, a training-free retrieval-augmented generation framework for flood damage nowcasting with reinforced reasoning. The framework builds a reasoning-centric kno…
FloodSQL-Bench: A Retrieval-Augmented Benchmark for Geospatially-Grounded Text-to-SQL
Hanzhou Liu, Kai Yin, Zhitong Chen +2
Existing Text-to-SQL benchmarks primarily focus on single-table queries or limited joins in general-purpose domains, and thus fail to reflect the complexity of domain-specific, mul…
WildfireGenome: Interpretable Machine Learning Reveals Local Drivers of Wildfire Risk and Their Cross-County Variation
Chenyue Liu, Ali Mostafavi
Current wildfire risk assessments rely on coarse hazard maps and opaque machine learning models that optimize regional accuracy while sacrificing interpretability at the decision s…
DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management
Kai Yin, Xiangjue Dong, Chengkai Liu +4
Effective and efficient access to relevant information is essential for disaster management. However, no retrieval model is specialized for disaster management, and existing genera…
Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models
Miguel Esparza, Archit Gupta, Kai Yin +2
The escalating intensity and frequency of wildfires demand innovative computational methods for rapid and accurate property damage assessment. Traditional methods are often time-co…
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management
Kai Yin, Xiangjue Dong, Chengkai Liu +5
Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on…
CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics
Kai Yin, Bo Li, Chengkai Liu +2
In the field of crisis/disaster informatics, social media is increasingly being used for improving situational awareness to inform response and relief efforts. Efficient and accura…
FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation
Chia-Fu Liu, Lipai Huang, Kai Yin +2
Near-real time estimation of damage to buildings and infrastructure, referred to as damage nowcasting in this study, is crucial for empowering emergency responders to make informed…
Beyond Quantities: Machine Learning-based Characterization of Inequality in Infrastructure Quality Provision in Cities
Bo Li, Ali Mostafavi
The objective of this study is to characterize inequality in infrastructure quality across urban areas. While a growing of body of literature has recognized the importance of chara…
FloodGenome: Interpretable Machine Learning for Decoding Features Shaping Property Flood Risk Predisposition in Cities
Chenyue Liu, Ali Mostafavi
Understanding the fundamental characteristics that shape the inherent flood risk disposition of urban areas is critical for integrated urban design strategies for flood risk reduct…
Rethinking Urban Flood Risk Assessment By Adapting Health Domain Perspective
Zhewei Liu, Kai Yin, Ali Mostafavi
Inspired by ideas from health risk assessment, this paper presents a new perspective for flood risk assessment. The proposed perspective focuses on three pillars for examining floo…
Decoding the Pulse of Community during Disasters: Resilience Analysis Based on Fluctuations in Latent Lifestyle Signatures within Human Visitation Networks
Junwei Ma, Ali Mostafavi
Examining the impact of disasters on life activities of populations is critical for understanding community resilience dynamics, yet it remains insufficiently studied in the existi…
Digital Divide in Disasters: Investigating Spatial and Socioeconomic Disparities in Internet Service Disruptions During Extreme Weather Events
Yuvraj Gupta, Zhewei Liu, Ali Mostafavi
The resilience of internet service is crucial for ensuring consistent communication, facilitating emergency response in digitally-dependent society. Due to empirical data constrain…
Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features
Kai Yin, Bo Li, Ali Mostafavi
Community resilience is a complex and muti-faceted phenomenon that emerges from complex and nonlinear interactions among different socio-technical systems and their resilience prop…
Unraveling Fundamental Properties of Power System Resilience Curves using Unsupervised Machine Learning
Bo Li, Ali Mostafavi
The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying infrastructure resilience. However, the theoret…
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning
Yu-Hsuan Ho, Zhewei Liu, Cheng-Chun Lee +1
Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditi…
Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas
Kai Yin, Junwei Ma, Ali Mostafavi
Urban flood risk emerges from complex and nonlinear interactions among multiple features related to flood hazard, flood exposure, and social and physical vulnerabilities, along wit…