Automatic Identification of Landslides Based on Deep Learning

A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification.

[1]  Q. Guo,et al.  Loess Landslide Detection Using Object Detection Algorithms in Northwest China , 2022, Remote. Sens..

[2]  Pedram Ghamisi,et al.  The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images , 2022, Big Earth Data.

[3]  S. Ullah,et al.  Extraction of Landslide Information Based on Object-Oriented Approach and Cause Analysis in Shuicheng, China , 2022, Remote. Sens..

[4]  S. Lu,et al.  A Novel Method for Extracting Time Series Information of Deformation Area of a Single Landslide Based on Improved U-Net Neural Network , 2021, Frontiers in Earth Science.

[5]  K. Hacıefendioğlu,et al.  Landslide detection using visualization techniques for deep convolutional neural network models , 2021, Natural Hazards.

[6]  Peng Liu,et al.  Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model , 2020, Remote. Sens..

[7]  Qing Zhu,et al.  Deep Fusion of Local and Non-Local Features for Precision Landslide Recognition , 2020, ArXiv.

[8]  Weile Li,et al.  Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks , 2020, Landslides.

[9]  Andrea Manconi,et al.  Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models , 2020, Remote. Sens..

[10]  K. N. Subramanya,et al.  An Approach to Real Time Parking Management using Computer Vision , 2019, Proceedings of the 2nd International Conference on Control and Computer Vision - ICCCV 2019.

[11]  Thomas Blaschke,et al.  Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection , 2019, Remote. Sens..

[12]  Dirk Tiede,et al.  DWELLING EXTRACTION IN REFUGEE CAMPS USING CNN – FIRST EXPERIENCES AND LESSONS LEARNT , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[13]  R. Czikhardt,et al.  Ground Stability Monitoring of Undermined and Landslide Prone Areas by Means of Sentinel-1 Multi-Temporal InSAR, Case Study from Slovakia , 2017 .

[14]  Tao Chen,et al.  Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China , 2017, Remote. Sens..

[15]  Lin Yan,et al.  Landslide mapping from aerial photographs using change detection-based Markov random field , 2016 .

[16]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Simon Plank,et al.  Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data , 2016, Remote. Sens..

[18]  Wenzhong Shi,et al.  Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method , 2016 .

[19]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Pedro Antonio Gutiérrez,et al.  Object-Based Image Classification of Summer Crops with Machine Learning Methods , 2014, Remote. Sens..

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  Mihai Datcu,et al.  Deep learning in very high resolution remote sensing image information mining communication concept , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[24]  Peng Liu,et al.  A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN , 2021, ISPRS Int. J. Geo Inf..

[25]  F. Catani,et al.  The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution , 2017, Landslides.

[26]  Amy Loutfi,et al.  Interactive Learning with Convolutional Neural Networks for Image Labeling , 2016, IJCAI 2016.