Abstract: Automatic landslide identification can solve the problem of slow speed of manual visual interpretation. The existing automatic identification methods based on deep learning are mainly single-task recognition methods such as object detection and semantic segmentation.In this paper, the instance segmentation network based on deep learning is used to explore a multi-task identification method that can achieve landslide target location and semantic segmentation simultaneously.Firstly, a dataset of 3822 loess landslide samples is constructed based on Google Earth images. Then,the multi-task automatic identification model of loess landslide based on small sample learning is constructed by using the one-stage instance segmentation network YOLACT. Finally, the identification results are evaluated by the large, medium and small scale landslide test samples. The results show as follows:①The average precision of landslide target positioning Box is 61.66%, the average precision of landslide semantic segmentation Mask is 62.0%, and the intersection over union of Mask in large scale test is 0.88. ②The landslide identification model built based on YOLACT can complete the dual-task identification of landslide target positioning and high-precision mask segmentation at the same time, which proride technical support for the automatic multi-task identification and rapid mapping of landslide.

Key words: landslide, automatic identification, deep learning, instance segmentation, YOLACT