Use of active learning for earthquake damage mapping from UAV photogrammetric point clouds

This article presents an effective classification method for earthquake damage mapping from unmanned aerial vehicles (UAV) photogrammetric point clouds. The classification method consists of three main components: (a) construction of a point feature descriptor regarding to spectral, textural, and geometrical features, (b) optimization of collecting informative training samples through an active learning (AL) method, and (c) fine-tuning the point-based classification results with contextual information. Besides using existing spectral and geometrical features, we design a textural feature based on fractal theory to construct a point feature descriptor through linear combination. A batch-model AL method called Margin Sampling and Multiclass Level Uncertainty (MS-MCLU) is proposed based on classification uncertainty using a Support Vector Machine classifier. We use a multi-label Markov random fields to fine-tune the point-based classification results with a pairwise model. The proposed method was tested using three sets of point clouds generated from UAV images over Mirabello, Lushan, and Wenchuan earthquake scenarios in 2012, Italy, and in 2013 and 2008, China, respectively. The proposed classification method was compared with that of two other feature descriptors, i. e. spectral combined with textural features (SpeTex) and geometrical features (Geo). The results show that classification accuracies were improved by using the proposed point feature descriptor. Results also show that the proposed MS-MCLU AL method evidently saved the cost of collecting informative training samples and produced higher classification accuracies than a random sampling strategy. Moreover, contextual information contributed to the improvement on the point-based classification results and was suggested to be considered in earthquake damage mapping applications.