arXiv subjects

Quoc Dung Cao

Publications and source records attributed to Quoc Dung Cao.

3 recordsLinked to original sources

arXiv2020

Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features

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 goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare-earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The dataset curated in this work is made openly available (DOI: 10.17603/ds2-3cca-f398).

cs.CV↗

arXiv2020

Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks

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 of flooded/damaged buildings, which is traditionally done by ground survey. This process can be labor-intensive and time-consuming. In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery. At the known building coordinates (available from public data), we extract square-sized images from the satellite imagery to create training, validation, and test datasets. Each square-sized image contains a building to be classified as either 'Flooded/Damaged' (labeled by volunteers in a crowd-sourcing project) or 'Undamaged'. We design and train a convolutional neural network from scratch and compare it with an existing neural network used widely for common object classification. We demonstrate the promise of our damage annotation model (over 97% accuracy) in the case study of building damage assessment in the Greater Houston area affected by 2017 Hurricane Harvey.

cs.CV↗

arXiv2019

Cross-Entropy Based Importance Sampling for Stochastic Simulation Models

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 deterministic simulation model, which maps an input to an output deterministically, and is approximated in practice using various methods. For the stochastic simulation model whose output is random given an input, the optimal importance sampling density was derived only recently. In the existing literature, metamodel-based approaches have been used to approximate this optimal density. However, building a satisfactory metamodel is often difficult or time-consuming in practice. This paper proposes a cross-entropy based method, which is automatic and does not require specific domain knowledge. The proposed method uses an expectation-maximization algorithm to guide the choice of a mixture distribution model for approximating the optimal density. The method iteratively updates the approximated density to minimize its estimated discrepancy, measured by estimated cross-entropy, from the optimal density. The mixture model's complexity is controlled using the cross-entropy information criterion. The method is empirically validated using extensive numerical studies and applied to a case study of evaluating the reliability of wind turbine using a stochastic simulation model.

stat.ME↗