|Table of Contents|

Landslide Intelligent Recognition Based on Multi-source Data Fusion(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2023年第04期
Page:
920-928
Research Field:
环境与可持续发展专刊
Publishing date:

Info

Title:
Landslide Intelligent Recognition Based on Multi-source Data Fusion
Author(s):
XIN Lu-bin1;  HAN Ling1; 2; 3*;  LI Liang-zhi1
(1. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China; 2. School of Land Engineering, Chang'an University, Xi'an 710054, Shaanxi, China; 3. Shaanxi Key Laboratory of Land Reclamation Engineering, Xi'an 710054, Shaanxi, China)
Keywords:
geological hazard;  landslide identification;  deep learning;  feature extraction;  semantic segmentation;  multi-source data;  residual network;  remote sensing
PACS:
P642.22; TP183
DOI:
-
Abstract:
The identification and investigation of landslide disasters are important basis for disaster prevention and mitigation. The traditional landslide recognition method has a low degree of automation, but the study on the intelligent landslide recognition model based on deep learning is very necessary for improving the accuracy and efficiency of landslide recognition. First of all, the optical remote sensing image, DEM data, geological data and rainfall data were introduced to construct landslide multi-source data set. Because multi-source heterogeneous data are not unified on the dimensional scale, the multi-source heterogeneous data preprocessing process and fusion model were designed. Secondly, the Res-UNet model is constructed as a network model for intelligent landslide recognition, and the training set and test set are divided by the ratio of 8:2. Finally, different data inputs are used for landslide recognition, and the Res-UNet model is compared with the mainstream semantic segmentation network models(FCN, U-Net and SegNet). The results show that multi-source data input has better landslide recognition results than single data input, and has more than 5% improvement in precision, recall, F1-score and MIoU; the precision of the landslide identification results of Res-UNet model is above 0.85, and it has excellent landslide identification, which can provide technical support for rapid and accurate identification of regional landslides.

References:

[1] 孙萍萍,张茂省,贾 俊,等.中国西部黄土区地质灾害调查研究进展[J].西北地质,2022,55(3):96-107.
SUN Ping-ping,ZHANG Mao-sheng,JIA Jun,et al.Geo-hazards Research and Investigation in the Loess Regions of Western China[J].Northwestern Geology,2022,55(3):96-107.
[2] HEIDARZADEH M,MIYAZAKI H,ISHIBE T,et al.Field Surveys of September 2018 Landslide-generated Waves in the Apporo Dam Reservoir,Japan:Combined Hazard from the Concurrent Occurrences of a Typhoon and an Earthquake[J].Landslides,2023,20(1):143-156.
[3] 李 阳,张建军,魏广阔,等.晋西黄土区极端降雨后浅层滑坡调查及影响因素分析[J].水土保持学报,2022,36(5):44-50.
LI Yang,ZHANG Jian-jun,WEI Guang-kuo,et al.Investigation of Shallow Landslide After Extreme Rainfall and Analysis of Its Influencing Factors in the West Shanxi Loess Region[J].Journal of Soil and Water Conservation,2022,36(5):44-50.
[4] CHEN Y Y,MING D P,LING X,et al.Landslide Susceptibility Mapping Using Feature Fusion-based CPCNN-ML in Lantau Island,Hong Kong[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2021,14:3625-3639.
[5] GIORDAN D,LUZI G,MONSERRAT O,et al.Remote Sensing Analysis of Geologic Hazards[J].Remote Sensing,2022,14(19):4818.
[6] LI L P,LAN H X,GUO C B,et al.A Modified Frequency Ratio Method for Landslide Susceptibility Assessment[J].Landslides,2017,14(2):727-741.
[7] LAN H X,ZHOU C H,WANG L J,et al.Landslide Hazard Spatial Analysis and Prediction Using GIS in the Xiaojiang Watershed,Yunnan,China[J].Engineering Geology,2004,76(1/2):109-128.
[8] 张 勤,白正伟,黄观文,等.GNSS滑坡监测预警技术进展[J].测绘学报,2022,51(10):1985-2000.
ZHANG Qin,BAI Zheng-wei,HUANG Guan-wen,et al.Review of GNSS Landslide Monitoring and Early Warning[J].Acta Geodaetica et Cartographica Sinica,2022,51(10):1985-2000.
[9] 朱 武,张 勤,朱建军,等.特大滑坡实时监测预警与技术装备研发[J].岩土工程学报,2022,44(7):1341-1350.
ZHU Wu,ZHANG Qin,ZHU Jian-jun,et al.Real-time Monitoring and Early Warning Technology for Huge Landslides[J].Chinese Journal of Geotechnical Engineering,2022,44(7):1341-1350.
[10] HE F,TAN S C,LIU H J.Mechanism of Rainfall Induced Landslides in Yunnan Province Using Multi-scale Spatio-temporal Analysis and Remote Sensing Interpretation[J].Microprocessors and Microsystems,2022,90:104502.
[11] KEYPORT R N,OOMMEN T,MARTHA T R,et al.A Comparative Analysis of Pixel- and Object-based Detection of Landslides from Very High-resolution Images[J].International Journal of Applied Earth Observation and Geoinformation,2018,64:1-11.
[12] 丁永辉,张 勤,杨成生,等.基于高分遥感的金沙江流域滑坡识别:以巴塘县王大龙村为例[J].测绘通报,2022(4):51-55.
DING Yong-hui,ZHANG Qin,YANG Cheng-sheng,et al.Landslide Identification in Jinsha River Basin Based on High-resolution Remote Sensing:Taking Wangdalong Village of Batang County as an Example[J].Bulletin of Surveying and Mapping,2022(4):51-55.
[13] 杨昭颖,韩灵怡,郑向向,等.基于卷积神经网络的遥感影像及DEM滑坡识别:以黄土滑坡为例[J].自然资源遥感,2022,34(2):224-230.
YANG Zhao-ying,HAN Ling-yi,ZHENG Xiang-xiang,et al.Landslide Identification Using Remote Sensing Images and DEM Based on Convolutional Neural Network:A Case Study of Loess Landslide[J].Remote Sensing for Natural Resources,2022,34(2):224-230.
[14] HACIEFENDIOGLU K,DEMIR G,BA �瘙 塁 AGA H B.Landslide Detection Using Visualization Techniques for Deep Convolutional Neural Network Models[J].Natural Hazards,2021,109(1):329-350.
[15] PANG D D,LIU G,HE J,et al.Automatic Remote Sensing Identification of Co-seismic Landslides Using Deep Learning Methods[J].Forests,2022,13(8):1213.
[16] 蔡浩杰,韩海辉,张雨莲,等.基于地形特征融合的卷积神经网络滑坡识别[J].地球科学与环境学报,2022,44(3):568-579.
CAI Hao-jie,HAN Hai-hui,ZHANG Yu-lian,et al.Convolutional Neural Network Landslide Recognition Based on Terrain Feature Fusion[J].Journal of Earth Sciences and Environment,2022,44(3):568-579.
[17] 刘 佳,伍宇明,高 星,等.基于GEE和U-Net模型的同震滑坡识别方法[J].地球信息科学学报,2022,24(7):1275-1285.
LIU Jia,WU Yu-ming,GAO Xing,et al.Image Recog-nition of Co-seismic Landslide Based on GEE and U-Net Neural Network[J].Journal of Geo-information Science,2022,24(7):1275-1285.
[18] WANG H J,ZHANG L M,YIN K S,et al.Landslide Identification Using Machine Learning[J].Geoscience Frontiers,2021,12(1):351-364.
[19] 曾 超,曹振宇,苏凤环,等.四川及周边滑坡泥石流灾害高精度航空影像及解译数据集(2008～2020年)[J].中国科学数据,2022,7(2):195-205.
ZENG Chao,CAO Zhen-yu,SU Feng-huan,et al.A Dataset of High-precision Aerial Imagery and Interpretation of Landslide and Debris Flow Disaster in Sichuan and Surrounding Areas Between 2008 and 2020[J].China Scientific Data,2022,7(2):195-205.
[20] 杨寒雨,赵晓永,王 磊.数据归一化方法综述[J].计算机工程与应用,2023,59(3):13-22.
YANG Han-yu,ZHAO Xiao-yong,WANG Lei.Revi-ew of Data Normalization Methods[J].Computer Engineering and Applications,2023,59(3):13-22.
[21] 张庭瑜,毛忠安,孙增慧.基于径向基神经网络耦合确定性指数的滑坡易发性分区研究[J].长江科学院院报,2021,38(11):64-72.
ZHANG Ting-yu,MAO Zhong-an,SUN Zeng-hui.Landslide Susceptibility Mapping Using Radial Basis Function Neural Network Coupling Certainty Factor[J].Journal of Yangtze River Scientific Research Institute,2021,38(11):64-72.
[22] HE K M,ZHANG X Y,REN S Q,et al.Deep Resi-dual Learning for Image Recognition[C]∥IEEE.2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas:IEEE,2016:770-778.
[23] WOO S,PARK J,LEE J Y,et al.CBAM:Convolutional Block Attention Module[C]∥FERRARI V,HEBERT M,SMINCHISESCU C,et al.Computer Vision-ECCV 2018.Munich:Springer,2018:3-19.
[24] SHAO Z F,ZHOU Z F,HUANG X,et al.MRENet:Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-resolution Images[J].Remote Sensing,2021,13(2):239.

Memo

Memo:
-
Last Update: 2023-06-20