35 citations · 41 across the 2 of their papers we have counts for
6 papers
Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features
Quoc Dung Cao, Youngjun Choe
Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goa…
Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion
Zhanlin Liu, Ashis G. Banerjee, Youngjun Choe
Sensitivity analysis (SA) is an important aspect of process automation. It often aims to identify the process inputs that influence the process output's variance significantly. Exi…
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
Sean Andrew Chen, Andrew Escay, Christopher Haberland +3
Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-…
Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
Quoc Dung Cao, Youngjun Choe
After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation. One way to gauge the damage extent is to quantify the number…
Cross-Entropy Based Importance Sampling for Stochastic Simulation Models
Quoc Dung Cao, Youngjun Choe
To efficiently evaluate system reliability based on Monte Carlo simulation, importance sampling is used widely. The optimal importance sampling density was derived in 1950s for the…
Data-Driven Sensitivity Indices for Models With Dependent Inputs Using the Polynomial Chaos Expansion
Zhanlin Liu, Youngjun Choe
Uncertainties exist in both physics-based and data-driven models. Variance-based sensitivity analysis characterizes how the variance of a model output is propagated from the model…