Using 3-D Convolution and Multimodal Architecture for Earthquake Damage Detection Based on Satellite Imagery and Digital Urban Data
Takashi Miyamoto, Yudai Yamamoto
Abstract
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 multitemporal satellite images is developed by applying a recent 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.