4/11/2023 0 Comments Uh multi sensor datasetFinally, seven famous machine learning (ML) algorithms-including support vector machine (SVM), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), decision trees (DT), k-nearest neighbors (KNN), and adaBoost (AB)-and a basic deep learning algorithm (i.e., multi-layer perceptron (MLP)) are implemented to obtain building damage maps. Then, a rule-based procedure is designed for the automatic selection of the proper training samples required by the classification algorithms in the next step. A “one-epoch convolutional autoencoder (OECAE)” is used to extract deep features from non-deep features. First, three different feature types-non-deep, deep, and their fusion-are investigated to determine the optimal feature extraction method. The method detects damages in four levels and consists of three steps. This paper proposes a novel deep-learning-based method for rapid post-earthquake building damage detection. Unmanned aerial vehicles (UAVs) have recently become very popular due to their agile deployment to sites, super-high spatial resolution, and relatively low operating cost. While satellite images have been used in the past two decades for building-damage mapping, they have rarely been utilized for the timely damage monitoring required for rescue operations. Immediately after an earthquake, rapid disaster management is the main challenge for relevant organizations.
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