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