Using 3D Convolution and Multimodal Architecture For Earthquake Damage Detection Based on Satellite Imagery and Digital Urban Data

Takashi Miyamoto, Yudai Yamamoto
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
When a large earthquake occurs, it is quite important to quickly figure out the damage distribution of housing structures for disaster prevention measures. Currently, the information is confirmed manually by local public organizations, which takes a lot of time. Therefore, a method is required for gathering the information more swiftly and objectively. In this work, a novel method for detecting damage to single buildings from a set of multi-temporal satellite images is developed by applying a
more » ... cent machine learning approach. The damage detection system is designed as a deep learning model that uses multimodal data, consisting of optical satellite images and structural attributes. The proposed method achieved over 90% detection accuracy on damaged housing in the affected area of 2016 Kumamoto earthquake, Japan from satellite images taken by Pleiades as well as digital urban data.
doi:10.1109/jstars.2021.3102701 fatcat:iz4katspnza2nom3ruw3byorki