Automatic Object Detection of Loess Landslide Based on Deep Learning

Abstract

Objectives The knowledge of regional landslides detection plays a fundamental role in the landslide risk management. However, most of that recognition was taken manually in the past, which is rather time- and labor- consuming. As the development of technologies of remote sensing and artificial intelligence, the automatic detection of landslides becomes possible. The previous researches relative to the automatic detection of landslides utilized the machine learning methods to detect these new landslides which were significantly distinguished from their context. Compared to those landslides, the detection of old loess landslides that are not distinct from their context is more challenged. We explore the deep learning to automatically detect the old loess landslides. Methods Firstly, we build a loess landslide database consists of 2 498 which are interpreted from the Google Earth images by experts. Then, we divide the database into three datasets for training, validation and test. Finally, we train Mask R-CNN object detection module with the training dataset, choose the best model by the validation dataset, and apply the best model to the test dataset. Results The test results of model performance show a precision of 0.56, a recall of 0.72, and a F1-score of 0.63. Conclusions The results indicate that Mask R-CNN is a robust method even for the detection of loess landslides that are unapparent from the context, and deep learning can provide the possibility for rapid and accurate regional geo-hazard investigation.