Damage assessment using remote sensing data with artificial intelligence

Depending on the magnitude of the earthquake, it can cause quite destructive effects. The magnitude 7.7 and 7.6 earthquakes that occurred on 6 February 2023 in Turkey can be given as an example. Hatay was heavily affected by these two major earthquakes and soon experienced a third earthquake of magnitude 6.4 on 20 February. In all three earthquakes, many people were injured or killed, and buildings were damaged or destroyed. Determining the damage caused by the earthquake raised the issue of coordinated and rapid delivery of rescue and relief activities to the region. The fact that the earthquakes occurred at short-term intervals revealed the fact that building damage assessment should be carried out immediately after the earthquake. In this study, images of Hatay province taken by Sentinel-2 satellite obtained by remote sensing method after the earthquake were used. The data obtained from these images were analysed by five algorithms (support vector machine, decision trees, Naive Bayes, k-NN, decision trees and ensemble) belonging to the supervised learning model, which is a type of machine learning method. K-Nearest Neighborhood (KNN) model achieved 85.1% success rate, Decision Tree (DT) model 81.6, support vector machine model %62.1, Navie Bayes %66.8 and ensemble model 86%. Thanks to the study, it has been observed that faster results are obtained by teaching the image reflection data of post-earthquake building damage detection to artificial intelligence. The aim of the study is to fulfil the coordination of relief activities quickly with the realisation of this function.