arXiv (Cornell University) · 2018 · 19 citations · 7 references
ComputingComputer ScienceComputer VisionMachine LearningRemote SensingDamage AssessmentDeep LearningImage ClassificationObject RecognitionRemote Sensing Image AnalysisSatellite Image InterpretationBuilding Damage AssessmentBuilding Damage ClassificationConvolutional Neural NetworksDamage Extent AssessmentDeep Learning-based ClassificationImage-based Damage AssessmentSatellite Image ClassificationSatellite Imagery Analysis
After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms. From the building coordinates, we extract their aerial-view windows of appropriate size and classify whether a building is damaged or not. We demonstrate the result of our method in the case study of 2017 Hurricane Harvey.
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Convolutional Neural Network Architectures for Matching Natural Language Sentences
Baotian Hu, Zhengdong Lu, Hang Li et al. · arXiv (Cornell University) · 2015 · 971 citations · Full text