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Found 13 papers, 4 papers with code
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
These results highlight the critical importance of modeling spatial heterogeneity in causal mechanisms for accurate disaster assessment, with direct implications for improving emergency response resource allocation.
Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference
These systems manifest in both static scenarios with instantaneous causal chains and temporal scenarios with evolving dynamics, complicating modeling efforts.
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts.
Spatial-variant causal Bayesian inference for rapid seismic ground failures and impacts estimation
Rapid and accurate estimation of post-earthquake ground failures and building damage is critical for effective post-disaster responses.
Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
Accurately assessing building damage is critical for disaster response and recovery.
Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models
Traditional systems for estimating human loss in disasters often depend on manually collected early casualty reports from global media, a process that's labor-intensive and slow with notable time delays.
Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate estimates are crucial for timely and effective post-disaster responses.
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
We gathered and annotated building damage ground truth data in Lee County, Florida, and compared the introduced method's estimation results with the ground truth and benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method.
HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis
Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge.
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning
The supervised learning requires historical structural response data and corresponding damage states (i. e., labels) for each building to learn the building-specific damage diagnosis model.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
In this work, we give new results on the benefits of multi-generator architecture of GANs.
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis
Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space.
