The Pix2PixHD model is used to transform the dance pose sequence into a real version of the dance. In addition, a generator module, a discriminator module, and a self-encoder module are added to make the dance movement smoother and consistent with the music. First, the sound and motion features are extracted from music and dance videos, and then, the model is built. A dance movement generation algorithm based on deep learning is designed to extract the mapping between sound and motion features to solve these problems. Moreover, new dance movements cannot be generated. The dance generated by the traditional music action matching and statistical mapping models is less consistent with the music itself. Additionally, it was found that the fusion of deep and non-deep features using OECAE could significantly enhance damage-mapping efficiency compared to those using either non-deep features (by an average improvement of 6.75% and 9.78% in OA and KC, respectively) or deep features (improving OA by 7.19% and KC by 10.18% on average) alone. The results indicated that auto-training samples are feasible and superior to manual ones, with improved overall accuracy (OA) and kappa coefficient (KC) over 22% and 33%, respectively SVM (OA = 82% and KC = 74.01%) was the most accurate AI model with a slight advantage over MLP (OA = 82% and KC = 73.98%). Finally, 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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