Review on remote sensing methods for landslide detection using machine and deep learning

[1]  Hyung-Sup Jung,et al.  Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing , 2019 .

[2]  Birgit Kleinschmit,et al.  Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan , 2017, Remote. Sens..

[3]  Omid Ghorbanzadeh,et al.  Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas , 2019, Remote. Sens..

[4]  Xianyu Yu,et al.  A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China , 2016, International journal of environmental research and public health.

[5]  F. Guzzetti,et al.  Landslide inventory maps: New tools for an old problem , 2012 .

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

[7]  Gong Jianya,et al.  Current issues in high-resolution earth observation technology , 2012 .

[8]  Mikhail Kanevski,et al.  Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping , 2013, Mathematical Geosciences.

[9]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Fuan Tsai,et al.  Post-disaster assessment of landslides in southern Taiwan after 2009 Typhoon Morakot using remote sensing and spatial analysis , 2010 .

[11]  S. Franklin,et al.  High Spatial Resolution Satellite Imagery, DEM Derivatives, and Image Segmentation for the Detection of Mass Wasting Processes , 2006 .

[12]  Atta-ur-Rahman,et al.  Telecommunication Networks in Disaster Management: A Review , 2019, J. Commun..

[13]  A. Parriaux,et al.  VALIDATION OF SPOT-5 SATELLITE IMAGERY FOR GEOLOGICAL HAZARD IDENTIFICATION AND RISK ASSESSMENT FOR LANDSLIDES , MUD AND DEBRIS FLOWS IN MATAGALPA , NICARAGUA , 2004 .

[14]  Monika Kuffer,et al.  The Temporal Dynamics of Slums Employing a CNN-Based Change Detection Approach , 2019, Remote. Sens..

[15]  Riccardo Lanari,et al.  Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy , 2014 .

[16]  Bernd Resch,et al.  Analysing and Predicting Micro-Location Patterns of Software Firms , 2017, ISPRS Int. J. Geo Inf..

[17]  S. Moretti,et al.  Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project , 2006 .

[18]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[19]  Yongchao Gong,et al.  Mask Scoring R-CNN , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Yong Li,et al.  Mass wasting triggered by the 2008 Wenchuan earthquake is greater than orogenic growth , 2011 .

[21]  Lixin Li,et al.  Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application , 2016, International journal of environmental research and public health.

[22]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  B. Pradhan,et al.  GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks , 2016, Environmental Earth Sciences.

[24]  Haokui Zhang,et al.  Deep learning for remote sensing image classification: A survey , 2018, WIREs Data Mining Knowl. Discov..

[25]  Chenming Li,et al.  Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data , 2019, Sensors.

[26]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[27]  P. Tarolli,et al.  Geomorphic features extraction from high-resolution topography: landslide crowns and bank erosion , 2012, Natural Hazards.

[28]  R. Soeters,et al.  Slope instability recognition, analysis, and zonation , 1996 .

[29]  Fuan Tsai,et al.  Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning , 2019, Sensors.

[30]  Arko Lucieer,et al.  Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[31]  Binh Thai Pham,et al.  Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS , 2016 .

[32]  Qingyun Du,et al.  Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China , 2019, International journal of environmental research and public health.

[33]  Yansheng Li,et al.  A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection , 2019, Applied Sciences.

[34]  Yu Huang,et al.  Review on landslide susceptibility mapping using support vector machines , 2018, CATENA.

[35]  C. Westen,et al.  Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake , 2010 .

[36]  Biswajeet Pradhan,et al.  Landslide Detection Using Residual Networks and the Fusion of Spectral and Topographic Information , 2019, IEEE Access.

[37]  Saro Lee,et al.  Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea , 2003 .

[38]  D. Petley,et al.  Combined Digital Photogrammetry and Time‐of‐Flight Laser Scanning for Monitoring Cliff Evolution , 2005 .

[39]  Y. Zhao,et al.  Comparison of decision tree methods for finding active objects , 2007, 0708.4274.

[40]  S. Bai,et al.  Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression , 2013, Environmental Earth Sciences.

[41]  S. P. Pradhan,et al.  Mass Wasting: An Overview , 2019 .

[42]  Pijush Samui,et al.  Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area , 2020 .

[43]  C. Irigaray,et al.  Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain) , 2012 .

[44]  S. M. de Jong,et al.  Airborne laser scanning of forested landslides characterization: terrain model quality and visualization , 2011 .

[45]  Richard Gloaguen,et al.  River Courses Affected by Landslides and Implications for Hazard Assessment: A High Resolution Remote Sensing Case Study in NE Iraq-W Iran , 2013, Remote. Sens..

[46]  S. Leroueil,et al.  The Varnes classification of landslide types, an update , 2014, Landslides.

[47]  Brett A. Bryan,et al.  A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change , 2019, Environ. Model. Softw..

[48]  K. Vinod Kumar,et al.  Damage and geological assessment of the 18 September 2011 Mw 6.9 earthquake in Sikkim, India using very high resolution satellite data , 2015 .

[49]  Sultan Kocaman,et al.  A Novel Performance Assessment Approach Using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest , 2019, Sensors.

[50]  Fabio Rocca,et al.  Permanent scatterers in SAR interferometry , 2001, IEEE Trans. Geosci. Remote. Sens..

[51]  Ming Zhu,et al.  A Review of Researches on Deep Learning in Remote Sensing Application , 2019, International Journal of Geosciences.

[52]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  Talal Ashraf Butt,et al.  Context‐aware cognitive disaster management using fog‐based Internet of Things , 2019, Trans. Emerg. Telecommun. Technol..

[54]  Jingxiao Cai,et al.  General purpose graphic processing unit implementation of adaptive pulse compression algorithms , 2017 .

[55]  Chong Xu,et al.  Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan Mw 7.9 earthquake of China and their spatial distribution statistical analysis , 2014, Landslides.

[56]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[57]  C. Gokceoğlu,et al.  Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province, Iran , 2014 .

[58]  Martino Pesaresi,et al.  Systematic Study of the Urban Postconflict Change Classification Performance Using Spectral and Structural Features in a Support Vector Machine , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[59]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[60]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[61]  T. Brewer,et al.  On the neutron absorption properties of basic and ultrabasic rocks: the significance of minor and trace elements , 2005, Geological Society, London, Special Publications.

[62]  Murat Ercanoglu,et al.  Assessment of ASTER satellite images in landslide inventory mapping: Yenice-Gökçebey (Western Black Sea Region, Turkey) , 2011 .

[63]  Taskin Kavzoglu,et al.  Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study , 2018, Landslides: Theory, Practice and Modelling.

[64]  Juliet M. Moringiello,et al.  Table of Contents. , 2017, Australian Journal of Primary Health.

[65]  Biswajeet Pradhan,et al.  Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree , 2016, Landslides.

[66]  Robert A. Schowengerdt,et al.  A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery , 1995 .

[67]  Biswajeet Pradhan,et al.  Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping , 2018, Sensors.

[68]  Dieu Tien Bui,et al.  Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction , 2019, Applied Sciences.

[69]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[70]  Marco Heurich,et al.  Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes , 2020, Remote. Sens..

[71]  Masahiro Chigira,et al.  Using an airborne laser scanner for the identification of shallow landslides and susceptibility assessment in an area of ignimbrite overlain by permeable pyroclastics , 2004 .

[72]  C. J. van Westen,et al.  Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories , 2012 .

[73]  S. Wan.  Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping , 2012, Environmental Earth Sciences.

[74]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[75]  Myeongsuk Pak,et al.  A review of deep learning in image recognition , 2017, 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT).

[76]  Fuxian Liu,et al.  Convolutional neural network based heterogeneous transfer learning for remote-sensing scene classification , 2019, International Journal of Remote Sensing.

[77]  Paraskevas Tsangaratos,et al.  Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment , 2020, CATENA.

[78]  P. Reichenbach,et al.  Comparing Landslide Maps: A Case Study in the Upper Tiber River Basin, Central Italy , 2000, Environmental management.

[79]  Lixin Guo,et al.  A Steam Ejector Refrigeration System Powered by Engine Combustion Waste Heat: Part 1. Characterization of the Internal Flow Structure , 2019, Applied Sciences.

[80]  Biswajeet Pradhan,et al.  A Novel Rule-Based Approach in Mapping Landslide Susceptibility , 2019, Sensors.

[81]  B. Pham,et al.  Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS , 2016, Natural Hazards.

[82]  Saro Lee,et al.  Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping , 2018, Remote. Sens..

[83]  Giovanni B. Crosta,et al.  Onset and timing of deep-seated gravitational slope deformations in the eastern Alps, Italy , 2009 .

[84]  A. Brenning.  Spatial prediction models for landslide hazards: review, comparison and evaluation , 2005 .

[85]  Derek T. Anderson,et al.  Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community , 2017 .

[86]  Xueliang Zhang,et al.  Deep learning in remote sensing applications: A meta-analysis and review , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[87]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[88]  Sabine Vanhuysse,et al.  Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery , 2019, Remote. Sens..

[89]  Wei Peng,et al.  Landslide Susceptibility Mapping Using Logistic Regression Analysis along the Jinsha River and Its Tributaries Close to Derong and Deqin County, Southwestern China , 2018, ISPRS Int. J. Geo Inf..

[90]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[91]  Yu Wang,et al.  Remote Sensing Landslide Recognition Based on Convolutional Neural Network , 2019, Mathematical Problems in Engineering.

[92]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[93]  Alexander Brenning,et al.  Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling , 2015, Comput. Geosci..

[94]  Navneet Walia,et al.  ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey , 2015 .

[95]  Joong-Sun Won,et al.  The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea , 2006 .

[96]  Lei Ma,et al.  Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning , 2020, Remote. Sens..

[97]  Gianfranco Fornaro,et al.  Advanced low- and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales , 2010 .

[98]  Hamid Reza Pourghasemi,et al.  Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms , 2018, Sustainability.

[99]  Yngvar Larsen,et al.  Detailed rockslide mapping in northern Norway with small baseline and persistent scatterer interferometric SAR time series methods. , 2010 .

[100]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[101]  Henri Eisenbeiss,et al.  The Autonomous Mini Helicopter : a Powerful Platform for Mobile Mapping , 2008 .

[102]  T. Martha,et al.  Identification of new Landslides from High Resolution Satellite Data Covering a Large Area Using Object-Based Change Detection Methods , 2016, Journal of the Indian Society of Remote Sensing.

[103]  Vicente Pérez-Muñuzuri,et al.  Extreme Wave Height Events in NW Spain: A Combined Multi-Sensor and Model Approach , 2017, Remote. Sens..

[104]  Seung-Rae Lee,et al.  A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea , 2016 .

[105]  Qiongji Jin,et al.  Design of Deep Belief Networks for Short-Term Prediction of Drought Index Using Data in the Huaihe River Basin , 2012 .

[106]  Wei Wei,et al.  Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction , 2018, International Journal of Computer Vision.

[107]  Nicola Casagli,et al.  Advanced radar-interpretation of InSAR time series for mapping and characterization of geological processes , 2011 .

[108]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[109]  Qingyong Zhang,et al.  Automatic recognition of landslide based on CNN and texture change detection , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[110]  Leonardo Santurri,et al.  Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images , 2011 .

[111]  Geoffrey I. Webb,et al.  Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series , 2018, Remote. Sens..

[112]  Eduardo Eiji Maeda,et al.  Landslide inventory using image fusion techniques in Brazil , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[113]  H. Modares,et al.  Combining PSO and k-means to enhance data clustering , 2008, 2008 International Symposium on Telecommunications.

[114]  Gong Cheng,et al.  Scene classification of high resolution remote sensing images using convolutional neural networks , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[115]  T. Kavzoglu,et al.  An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district , 2015, Natural Hazards.

[116]  B. Saikia,et al.  Elemental geochemistry and mineralogy of coals and associated coal mine overburden from Makum coalfield (Northeast India) , 2016, Environmental Earth Sciences.

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

[118]  P. Reichenbach,et al.  Comparing landslide inventory maps , 2008 .

[119]  Jonathan Li,et al.  Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[120]  Gunasekaran Manogaran,et al.  Revamping land coverage analysis using aerial satellite image mapping , 2021, Trans. Emerg. Telecommun. Technol..

[121]  M. Jaboyedoff,et al.  Characterization and monitoring of the Åknes rockslide using terrestrial laser scanning , 2009 .

[122]  Omid Ghorbanzadeh,et al.  UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks , 2019, Remote. Sens..

[123]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[124]  Chiang Wei,et al.  Locating landslides using multi-temporal satellite images , 2004 .

[125]  Ali P. Yunus,et al.  Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan , 2019, Landslides.

[126]  Denys Brunsden,et al.  Mass movement; the research frontier and beyond: a geomorphological approach , 1993 .

[127]  Biswajeet Pradhan,et al.  Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster-Shafer , 2018, Remote. Sens..

[128]  Peng Gao,et al.  Landslide mapping with remote sensing: challenges and opportunities , 2020, International Journal of Remote Sensing.

[129]  Dhavalkumar Thakker,et al.  An ontology-based system for discovering landslide-induced emergencies in electrical grid , 2020, Trans. Emerg. Telecommun. Technol..

[130]  Jian Wang,et al.  A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment , 2019, Sensors.

[131]  Biswajeet Pradhan,et al.  A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) , 2016, Environ. Model. Softw..

[132]  Umaa Rebbapragada,et al.  Object-based classification of earthquake damage from high-resolution optical imagery using machine learning , 2016 .

[133]  Grant.  Living with landslide risk in Europe: Assessment, effects of global change, and risk management strategies , 2010 .

[134]  Z. Zou,et al.  Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant , 2019, Mathematical Problems in Engineering.

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

[136]  Thomas Blaschke,et al.  Optimizing Sample Patches Selection of CNN to Improve the mIOU on Landslide Detection , 2019, GISTAM.