Property-Level Flood Risk Assessment Using AI-Enabled Street-View Lowest Floor Elevation Extraction and ML Imputation Across Texas

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-specific elevation data at regional scale for flood risk assessment. We develop and apply a three-stage pipeline across 18 areas of interest (AOIs) in Texas that (1) extracts LFE and the height difference between street grade and the lowest floor (HDSL) from Google Street View imagery using the Elev-Vision framework, (2) imputes missing HDSL values with Random Forest and Gradient Boosting models trained on 16 terrain, hydrologic, geographic, and flood-exposure features, and (3) integrates the resulting elevation dataset with Fathom 1-in-100 year inundation surfaces and USACE depth-damage functions to estimate property-specific interior flood depth and expected loss. Across 12,241 residential structures, street-view imagery was available for 73.4% of parcels and direct LFE/HDSL extraction was successful for 49.0% (5,992 structures). Imputation was retained for 13 AOIs where cross-validated performance was defensible, with selected models achieving R suqre values from 0.159 to 0.974; five AOIs were explicitly excluded from prediction because performance was insufficient. The results show that street-view-based elevation mapping is not universally available for every property, but it is sufficiently scalable to materially improve regional flood-risk characterization by moving beyond hazard exposure to structure-level estimates of interior inundation and expected damage. Scientifically, the study advances LFE estimation from a pilot-scale proof of concept to a regional, end-to-end workflow. Practically, it offers a replicable framework for jurisdictions that lack comprehensive Elevation Certificates but need parcel-level information to support mitigation, planning, and flood-risk management.

R2RAG-Flood: A reasoning-reinforced training-free retrieval augmentation generation framework for flood damage nowcasting

R2RAG-Flood is a reasoning-reinforced, training-free retrieval-augmented generation framework for post-storm property damage nowcasting. Building on an existing supervised tabular predictor, the framework constructs a reasoning-centric knowledge base composed of labeled tabular records, where each sample includes structured predictors, a compact natural language text-mode summary, and a model-generated reasoning trajectory. During inference, R2RAG-Flood issues context-augmented prompts that retrieve and condition on relevant reasoning trajectories from nearby geospatial neighbors and canonical class prototypes, enabling the large language model backbone to emulate and adapt prior reasoning rather than learn new task-specific parameters. Predictions follow a two-stage procedure that first determines property damage occurrence and then refines severity within a three-level Property Damage Extent categorization, with a conditional downgrade step to correct over-predicted severity. In a case study of Harris County, Texas at the 12-digit Hydrologic Unit Code scale, the supervised tabular baseline trained directly on structured predictors achieves 0.714 overall accuracy and 0.859 damage class accuracy for medium and high damage classes. Across seven large language model backbones, R2RAG-Flood attains 0.613 to 0.668 overall accuracy and 0.757 to 0.896 damage class accuracy, approaching the supervised baseline while additionally producing a structured rationale for each prediction. Using a severity-per-cost efficiency metric derived from API pricing and GPU instance costs, lightweight R2RAG-Flood variants demonstrate substantially higher efficiency than both the supervised tabular baseline and larger language models, while requiring no task-specific training or fine-tuning.

DisastQA: A Comprehensive Benchmark for Evaluating Question Answering in Disaster Management

Accurate question answering (QA) in disaster management requires reasoning over uncertain and conflicting information, a setting poorly captured by existing benchmarks built on clean evidence. We introduce DisastQA, a large-scale benchmark of 3,000 rigorously verified questions (2,000 multiple-choice and 1,000 open-ended) spanning eight disaster types. The benchmark is constructed via a human-LLM collaboration pipeline with stratified sampling to ensure balanced coverage. Models are evaluated under varying evidence conditions, from closed-book to noisy evidence integration, enabling separation of internal knowledge from reasoning under imperfect information. For open-ended QA, we propose a human-verified keypoint-based evaluation protocol emphasizing factual completeness over verbosity. Experiments with 20 models reveal substantial divergences from general-purpose leaderboards such as MMLU-Pro. While recent open-weight models approach proprietary systems in clean settings, performance degrades sharply under realistic noise, exposing critical reliability gaps for disaster response. All code, data, and evaluation resources are available at https://github.com/TamuChen18/DisastQA_open.

GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface

As wildfires increasingly evolve into urban conflagrations, traditional risk models that treat structures as isolated assets fail to capture the non-linear contagion dynamics characteristic of the wildland urban interface (WUI). This research bridges the gap between mechanistic physics and data driven learning by establishing a novel dual specialist ensemble framework that disentangles vulnerability into two distinct vectors, environmental contagion and structural fragility. The architecture integrates two specialized predictive streams, an environmental specialist, implemented as a graph neural network (GNN) that operationalizes the community as a directed contagion graph weighted by physics informed convection, radiation, and ember probabilities, and enriched with high dimensional Google AlphaEarth Foundation embeddings, and a Structural Specialist, implemented via XGBoost to isolate granular asset level resilience. Applied to the 2025 Eaton Fire, the framework reveals a critical dichotomy in risk drivers. The GNN demonstrates that neighborhood scale environmental pressure overwhelmingly dominates intrinsic structural features in defining propagation pathways, while the XGBoost model identifies eaves as the primary micro scale ingress vector. By synthesizing these divergent signals through logistic stacking, the ensemble achieves robust classification and generates a diagnostic risk topology. This capability empowers decision makers to move beyond binary loss prediction and precisely target mitigation prioritizing vegetation management for high connectivity clusters and structural hardening for architecturally vulnerable nodes thereby operationalizing a proactive, data driven approach to community resilience.

FloodSQL-Bench: A Retrieval-Augmented Benchmark for Geospatially-Grounded Text-to-SQL

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, multi-table and geospatial reasoning, To address this limitation, we introduce FLOODSQL-BENCH, a geospatially grounded benchmark for the flood management domain that integrates heterogeneous datasets through key-based, spatial, and hybrid joins. The benchmark captures realistic flood-related information needs by combining social, infrastructural, and hazard data layers. We systematically evaluate recent large language models with the same retrieval-augmented generation settings and measure their performance across difficulty tiers. By providing a unified, open benchmark grounded in real-world disaster management data, FLOODSQL-BENCH establishes a practical testbed for advancing Text-to-SQL research in high-stakes application domains.

Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery

Most post-disaster damage classifiers succeed only when destructive forces leave clear spectral or structural signatures -- conditions rarely present after inundation. Consequently, existing models perform poorly at identifying flood-related building damages. The model presented in this study, Flood-DamageSense, addresses this gap as the first deep-learning framework purpose-built for building-level flood-damage assessment. The architecture fuses pre- and post-event SAR/InSAR scenes with very-high-resolution optical basemaps and an inherent flood-risk layer that encodes long-term exposure probabilities, guiding the network toward plausibly affected structures even when compositional change is minimal. A multimodal Mamba backbone with a semi-Siamese encoder and task-specific decoders jointly predicts (1) graded building-damage states, (2) floodwater extent, and (3) building footprints. Training and evaluation on Hurricane Harvey (2017) imagery from Harris County, Texas -- supported by insurance-derived property-damage extents -- show a mean F1 improvement of up to 19 percentage points over state-of-the-art baselines, with the largest gains in the frequently misclassified "minor" and "moderate" damage categories. Ablation studies identify the inherent-risk feature as the single most significant contributor to this performance boost. An end-to-end post-processing pipeline converts pixel-level outputs to actionable, building-scale damage maps within minutes of image acquisition. By combining risk-aware modeling with SAR's all-weather capability, Flood-DamageSense delivers faster, finer-grained, and more reliable flood-damage intelligence to support post-disaster decision-making and resource allocation.

DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management

Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management scenarios. All source codes and DisastIR are available at https://github.com/KaiYin97/Disaster_IR.

DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization

Natural disasters increasingly threaten communities worldwide, creating an urgent need for rapid, reliable building damage assessment to guide emergency response and recovery efforts. Current methods typically classify damage in binary (damaged/undamaged) or ordinal severity terms, limiting their practical utility. In fact, the determination of damage typology is crucial for response and recovery efforts. To address this important gap, this paper introduces DamageCAT, a novel framework that provides typology-based categorical damage descriptions rather than simple severity ratings. Accordingly, this study presents two key contributions: (1) the BD-TypoSAT dataset containing satellite image triplets (pre-disaster, post-disaster, and damage masks) from Hurricane Ida with four damage categories (partial roof damage, total roof damage, partial structural collapse, and total structural collapse), and (2) a hierarchical U-Net-based transformer architecture that effectively processes pre-post disaster image pairs to identify and categorize building damage. Despite significant class imbalances in the training data, our model achieved robust performance with overall metrics of 0.7921 Intersection over Union (IoU) and 0.8835 F1 scores across all categories. The model's capability to recognize intricate damage typology in less common categories is especially remarkable. The DamageCAT framework advances automated damage assessment by providing actionable, typological information that better supports disaster response decision-making and resource allocation compared to traditional severity-based approaches.

A System for Critical Facility and Resource Optimization in Disaster Management and Planning

Disruptions to medical infrastructure during disasters pose significant risks to critically ill patients with advanced chronic kidney disease or end-stage renal disease. To enhance patient access to dialysis treatment under such conditions, it is crucial to assess the vulnerabilities of critical care facilities to hazardous events. This study proposes optimization models for patient reallocation and the strategic placement of temporary medical facilities to bolster the resilience of the critical care system, with a focus on equitable outcomes. Utilizing human mobility data from Texas, we evaluate patient access to critical care and dialysis centers under simulated hazard scenarios. The proposed bio-inspired optimization model, based on the Ant Colony optimization method, efficiently reallocates patients to mitigate disrupted access to dialysis facilities. The model outputs offer valuable insights into patient and hospital preparedness for disasters. Overall, the study presents a data-driven, analytics-based decision support tool designed to proactively mitigate potential disruptions in access to critical care facilities during disasters, tailored to the needs of health officials, emergency managers, and hospital system administrators in both the private and public sectors.

High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data

High-resolution flood probability maps are essential for addressing the limitations of existing flood risk assessment approaches but are often limited by the availability of historical event data. Also, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant computation and time effort inhibiting the feasibility. To address this gap, this study introduces Flood-Precip GAN (Flood-Precipitation Generative Adversarial Network), a novel methodology that leverages generative machine learning to simulate large-scale synthetic inundation data to produce probabilistic flood maps. With a focus on Harris County, Texas, Flood-Precip GAN begins with training a cell-wise depth estimator using a limited number of physics-based model-generated precipitation-flood events. This model, which emphasizes precipitation-based features, outperforms universal models. Subsequently, a Generative Adversarial Network (GAN) with constraints is employed to conditionally generate synthetic precipitation records. Strategic thresholds are established to filter these records, ensuring close alignment with true precipitation patterns. For each cell, synthetic events are smoothed using a K-nearest neighbors algorithm and processed through the depth estimator to derive synthetic depth distributions. By iterating this procedure and after generating 10,000 synthetic precipitation-flood events, we construct flood probability maps in various formats, considering different inundation depths. Validation through similarity and correlation metrics confirms the fidelity of the synthetic depth distributions relative to true data. Flood-Precip GAN provides a scalable solution for generating synthetic flood depth data needed to create high-resolution flood probability maps, significantly enhancing flood preparedness and mitigation efforts.