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This paper presents a novel method of landslide detection by exploiting theMask R-CNN capability of identifying an object layout by using a pixel-basedsegmentation, along with transfer learning used to train the proposed model. Adata set of 160 elements is created containing landslide and non-landslideimages. The proposed method consists of three steps: (i) augmenting trainingimage samples to increase the volume of the training data, (ii) fine tuningwith limited image samples, and (iii) performance evaluation of the algorithmin terms of precision, recall and F1 measure, on the considered landslideimages, by adopting ResNet-50 and 101 as backbone models. The experimentalresults are quite encouraging as the proposed method achieves Precision equalsto 1.00, Recall 0.93 and F1 measure 0.97, when ResNet-101 is used as backbonemodel, and with a low number of landslide photographs used as training samples.The proposed algorithm can be potentially useful for land use planners andpolicy makers of hilly areas where intermittent slope deformations necessitatelandslide detection as prerequisite before planning.

A New Mask R-CNN Based Method for Improved Landslide Detection