An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia
Abstract
:1. Introduction
2. Data and Methods
2.1. Satellite Imagery Data
2.2. Artificial Neural Network
2.3. Support Vector Machine
2.4. Decorrelation Method
3. Results
3.1. Landsat Image Classification of ANN and SVM Classifiers
3.2. Landsat Images: Decorrelation of the ANN and SVM Classifiers
3.3. Sentinel Image Classification using ANN and SVM Classifiers
3.4. Sentinel Image Decorrelation of ANN and SVM Classifiers
3.5. Conformity of the ANN- and SVM-derived Damage Maps based on Landsat and Sentinel Images
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Syifa, M.; Kadavi, P.R.; Lee, C.-W. An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia. Sensors 2019, 19, 542. https://doi.org/10.3390/s19030542
Syifa M, Kadavi PR, Lee C-W. An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia. Sensors. 2019; 19(3):542. https://doi.org/10.3390/s19030542
Chicago/Turabian StyleSyifa, Mutiara, Prima Riza Kadavi, and Chang-Wook Lee. 2019. "An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia" Sensors 19, no. 3: 542. https://doi.org/10.3390/s19030542