SAR and LIDAR Datasets for Building Damage Evaluation Based on Support Vector Machine and Random Forest Algorithms—A Case Study of Kumamoto Earthquake, Japan

• Published in: Applied sciences Vol. 10; no. 24; p. 8932
• Main Authors: Hajeb, Masoud, Karimzadeh, Sadra, Matsuoka, Masashi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14-12-2020
• Subjects: airborne SAR; Algorithms; Buildings; Damage assessment; Damage detection; earthquake; Earthquake damage; Earthquakes; Kumamoto; Landslides & mudslides; Learning algorithms; LIDAR; Machine learning; Natural disasters; Radar detection; Remote sensing; Seismic activity; Statistical analysis; Support vector machines; Synthetic aperture radar; Japan
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10248932

The evaluation of buildings damage following disasters from natural hazards is a crucial step in determining the extent of the damage and measuring renovation needs. In this study, a combination of the synthetic aperture radar (SAR) and light detection and ranging (LIDAR) data before and after the earthquake were used to assess the damage to buildings caused by the Kumamoto earthquake. For damage assessment, three variables including elevation difference (ELD) and texture difference (TD) in pre- and post-event LIDAR images and coherence difference (CD) in SAR images before and after the event were considered and their results were extracted. Machine learning algorithms including random forest (RDF) and the support vector machine (SVM) were used to classify and predict the rate of damage. The results showed that ELD parameter played a key role in identifying the damaged buildings. The SVM algorithm using the ELD parameter and considering three damage rates, including D0 and D1 (Negligible to slight damages), D2, D3 and D4 (Moderate to Heavy damages) and D5 and D6 (Collapsed buildings) provided an overall accuracy of about 87.1%. In addition, for four damage rates, the overall accuracy was about 78.1%.