Analysis of Damage to Buildings affected by the Tsunami in the Palu Coastal Area Using Deep Learning

Irwan Meilano, Achmad Ikbal Rahadian, Deni Suwardhi, Wulan Suminar, Fiza Wira Atmaja, Cecep Pratama, Euis Sunarti, Setya Haksama

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Assessing the building damage after a tsunami is the first step to quantitatively learn about the amount of damage it caused. Indonesia is an archipelagic country, with two-thirds of its territory consisting of water. It has the second-longest coastline in the world, increasing the potential for tsunami damage in Indonesian territory. In this study, an analysis of building damage due to the tsunamis was carried out and Palu was assigned as the study location. Palu's coastal area suffered a tsunami on September 28, 2018, caused by an earthquake with a magnitude of 7.5. The location and the number of buildings were generated through object detection using deep learning from high-resolution satellite imagery data. Object detection was carried out using pre-trained YOLOv3 models that are trained using 315 satellite images as data sets and produce a model with a loss value of 33.15. Object detection was carried out on satellite imagery before and after the tsunami and produced building distribution data with an accuracy of 76.78% and 74.20%, respectively. Comparisons of building data detected from the two satellite images were then analyzed using a tsunami height zone map to see the correlation between building damage and tsunami height. From spatial and correlations analysis, 1,547 damaged buildings were detected, giving the data a positive correlation type. Using the student's t-test, it was concluded that there was a significant correlation between building damage and tsunami height.

Original languageEnglish
Title of host publicationProceeding - AGERS 2020
Subtitle of host publicationIEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology: Understanding the Interaction of Land, Ocean and Atmosphere: Disaster Mitigation and Regional Resillience
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages95-97
Number of pages3
ISBN (Electronic)9780738144320
DOIs
Publication statusPublished - 7 Dec 2020
Event3rd IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2020 - Jakarta, Indonesia
Duration: 7 Dec 20208 Dec 2020

Publication series

NameProceeding - AGERS 2020: IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology: Understanding the Interaction of Land, Ocean and Atmosphere: Disaster Mitigation and Regional Resillience

Conference

Conference3rd IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2020
Country/TerritoryIndonesia
CityJakarta
Period7/12/208/12/20

Keywords

  • Palu
  • Tsunami
  • building damage
  • deep learning
  • satellite imagery

Fingerprint

Dive into the research topics of 'Analysis of Damage to Buildings affected by the Tsunami in the Palu Coastal Area Using Deep Learning'. Together they form a unique fingerprint.

Cite this