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.
Highlights
P REPAREDNESS for and mitigation of natural disasters that frequently occur throughout the world is one of society’s most pressing problems
It is clear that the use of time-series satellite images is effective for detecting disaster damage
The deep learning model is considered to have acquired the features that contribute to earthquake damage detection through the learning process
Summary
P REPAREDNESS for and mitigation of natural disasters that frequently occur throughout the world is one of society’s most pressing problems. The response phase refers to activities conducted immediately after the disaster, such as search and rescue operations and the provisioning of necessary supplies. These activities are important for earthquakes where the complete prevention of damage is difficult. Promptly identifying the state of damage—the earthquake location and damage degree—is crucial, as it enables the appropriate allocation of resources and sound decision-making by individuals and disaster support organizations. Such a prompt assessment can optimize subsequent activities
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