Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples

Abstract

Deep learning has been widely used in landslides detection. However, in practical application, the sample quality often cannot meet the requirements of training models. Some smaller landslides are easy to be omitted if there are multiple landslide objects in one sample. Furthermore, there are some objects with similar shape, texture and colour to landslides (complex backgrounds), such as bare land, roads, water surfaces and artificial buildings. The traditional landslides detection method is easy to confuse landslides and complex backgrounds, which leads to false and omissive detections. To solve the above two problems, a complex background enhancement method with multi-scale samples (MSSCBE) was proposed to improve sample quality. Using the background enhanced samples, the deep learning model can not only learn differences between landslides and complex backgrounds, but also learn the multi-scale features of landslides better. The proposed method was applied to detect landslides that occurred in Jiuzhaigou County, Sichuan Province. Comparative experiments were conducted using Mask R-CNN model. And the model trained with both MSSCBE background enhanced samples and original samples has the best performance. Compared with the model trained with only original samples, Precision, Recall, F1 Score and mIoU is improved by 29.76%, 5.59%, 17.82% and 25.80%, respectively.