Classifying buildings post hurricane with contrast enhancement

Jinsheng Luo; Jinzhou Yu; Siqi Yu

doi:10.1117/12.2641123

13 October 2022 Classifying buildings post hurricane with contrast enhancement

Jinsheng Luo, Jinzhou Yu, Siqi Yu

Author Affiliations +

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871J (2022) https://doi.org/10.1117/12.2641123
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

In the aftermath of a hurricane, damage assessment is critical for relief helpers. One way to measure damage is to detect and quantify the number of damaged structures, usually by driving around the affected area and noting them down manually. This process can be labor-intensive and time-consuming and is not the most efficient method. Therefore, in this paper, we choose to apply image classification to improve the efficiency of assessing damaged building. From the public satellite imagery data of building, we extract square-sized images and create training, validation, test, and unbalanced test datasets. Each square-sized image contains a building to be classified as either ‘damage’ or ' no damage’. We use the existing convolutional neural network model to classify the datasets, and then use the results to modify our algorithm to achieve better programs. We apply our final model to the dataset that is satellite image from Texas after Hurricane Harvey with 99% accuracy to demonstrate the efficiency of assessing damaged buildings.

Citation Download Citation

Jinsheng Luo, Jinzhou Yu, and Siqi Yu "Classifying buildings post hurricane with contrast enhancement", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871J (13 October 2022); https://doi.org/10.1117/12.2641123

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