[31st AIP Open Seminar] Talks by Disaster Resilience Science Team

Disaster Resilience Science Team (https://aip.riken.jp/labs/goalorient_tech/disaster_resilience/) at RIKEN AIP

The first, second and third sessions will be delivered in Japanese. The fourth session will be provided in English. (Simultaneous interpretation will not be available.)
If you want to join the English session, please join this seminar after around 4:20 pm JST.

Speaker 1: 上田修功 (35 min)
Title: 防災科学チームの概要と気象予測について(Japanese)

Speaker 2: 市村強 (25 min)
Title: 高性能計算による地震の超大規模シミュレーションとそのニューラルネットワークによる高速化(Japanese)

Speaker 3: 岡崎智久 (25 min)
Title: データ駆動による地震動強さの予測式(Japanese)

Speaker 4: Bahareh Kalantar (25 min)
Title: Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images
Abstract: In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.

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