Fast Seismic Landslide Detection Based on Improved Mask R-CNN

Abstract

For emergency rescue and damage assessment after an earthquake, quick detection of seismic landslides in the affected areas is crucial. The purpose of this study is to quickly determine the extent and size of post-earthquake seismic landslides using a small amount of post-earthquake seismic landslide imagery data. This information will serve as a foundation for emergency rescue efforts, disaster estimation, and other actions. In this study, Wenchuan County, Sichuan Province, China’s 2008 post-quake Unmanned Air Vehicle (UAV) remote sensing images are used as the data source. ResNet-50, ResNet-101, and Swin Transformer are used as the backbone networks of Mask R-CNN to train and identify seismic landslides in post-quake UAV images. The training samples are then augmented by data augmentation methods, and transfer learning methods are used to reduce the training time required and enhance the generalization of the model. Finally, transfer learning was used to apply the model to seismic landslide imagery from Haiti after the earthquake that was not calibrated. With Precision and F1 scores of 0.9328 and 0.9025, respectively, the results demonstrate that Swin Transformer performs better as a backbone network than the original Mask R-CNN, YOLOv5, and Faster R-CNN. In Haiti’s post-earthquake images, the improved model performs significantly better than the original model in terms of accuracy and recognition. The model for identifying post-earthquake seismic landslides developed in this paper has good generalizability and transferability as well as good application potential in emergency responses to earthquake disasters, which can offer strong support for post-earthquake emergency rescue and disaster assessment.

Recent Trends and advances in deep learning techniques for the classification of landslides using satellite images: comprehensive survey

“…Fu R et al [24] proposed a study on post-earthquake seismic landslides. To determine the size of post-earthquake few images of post-earthquake seismic landslide satellite imagery data were used.…”

“…[22]. The Swis Transformer as backbone network with Mask R-CNN claims 82.2% accuracy [24] x Method used: Convolutional neural network architecture such as ResNet50, ResNet101, VGG, DensNet, Google Net were used as the backbone with different approaches and provide different results. CNN along with spatial channel attention mechanism and high-resolution optical images yield high accuracy.…”

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

“…Currently, we are facing an increasing demand for detailed and accurate landslide maps and inventories from around the world [4][5][6]. Especially in areas prone to landslides or earthquake disasters, it is of great significance to quickly and accurately obtain an inventory of landslides over a large area for disaster prevention and relief [7][8][9][10][11][12][13][14]. It is also important to promptly determine the location of landslides and their area of influence and to take targeted action to minimize the damage caused by landslide disasters [15].…”

Enhance the Accuracy of Landslide Detection in UAV Images Using an Improved Mask R-CNN Model: A Case Study of Sanming, China

“…However, the processing of landslide data is more complicated. Fu et al [36] used the method of transfer learning to build a recognition model of UAV landslide images after the Wenchuan earthquake, and successfully applied it to the Haiti earthquake landslide; however, the model parameters are too many and the computer performance requirements are too high. Kubo et al [37] aimed to explore the impact of different image enhancement modes on detection accuracy.…”