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Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in practical applications. In contrast, optical images can contain more information on slope displacement at a much lower cost. Therefore, a low-cost, high-spatial-resolution and easy-to-implement landslide surface deformation monitoring system based on close-range photogrammetry is developed in this paper. The proposed system leverages multiple image processing methods and monocular visual localization, combined with machine learning, to ensure accurate monitoring under time series. The results of several laboratory landslide experiments show that the proposed system achieved millimeter-level monitoring accuracy in laboratory landslide experiments. Moreover, the proposed system could capture slow displacement precursors of 5 mm to 10 mm before significant landslide failure occurred, which provides favorable surface deformation evidence for landslide monitoring and early warning. In addition, the system was deployed on a natural slope in Lanzhou, yielding preliminary effective monitoring results. The laboratory experimental results demonstrated the system\u2019s effectiveness and high accuracy in monitoring landslide surface deformation, particularly its significant application value in early warning. The field deployment results indicated that the system could also effectively provide data support in natural environments, offering practical evidence for landslide monitoring and warning.<\/jats:p>","DOI":"10.3390\/rs16234380","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T08:38:24Z","timestamp":1732523904000},"page":"4380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5500-5845","authenticated-orcid":false,"given":"Tianxin","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"Faculty of Geosciences and Environment, University of Lausanne, 1015 Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9997-8505","authenticated-orcid":false,"given":"Peng","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"Guangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5123-5802","authenticated-orcid":false,"given":"Wei","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangshuang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangling","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"Guangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"Safe Urban Development Institute of Science and Technology (Shenzhen), Shenzhen 518023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"An","sequence":"additional","affiliation":[{"name":"Gansu Institute of Engineering Geology, Lanzhou 730099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangjun","family":"Li","sequence":"additional","affiliation":[{"name":"Gansu Institute of Engineering Geology, Lanzhou 730099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"BingBing","family":"Han","sequence":"additional","affiliation":[{"name":"Gansu Institute of Engineering Geology, Lanzhou 730099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baofeng","family":"Wan","sequence":"additional","affiliation":[{"name":"Gansu Institute of Engineering Geology, Lanzhou 730099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruidong","family":"Li","sequence":"additional","affiliation":[{"name":"Gansu Institute of Engineering Geology, Lanzhou 730099, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.earscirev.2016.08.011","article-title":"Landslides in a Changing Climate","volume":"162","author":"Gariano","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s43017-022-00373-x","article-title":"Landslide Detection, Monitoring and Prediction with Remote-Sensing Techniques","volume":"4","author":"Casagli","year":"2023","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Lu, Z. (2018). Remote Sensing of Landslides\u2014A Review. Remote Sens., 10.","DOI":"10.3390\/rs10020279"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1007\/s10346-022-02007-1","article-title":"Landslide Monitoring Techniques in the Geological Surveys of Europe","volume":"20","author":"Herrera","year":"2023","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Strz\u0105ba\u0142a, K., \u0106wi\u0105ka\u0142a, P., and Puniach, E. (2024). Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing. Remote Sens., 16.","DOI":"10.3390\/rs16152781"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103574","DOI":"10.1016\/j.earscirev.2021.103574","article-title":"Landslide Failures Detection and Mapping Using Synthetic Aperture Radar: Past, Present and Future","volume":"216","author":"Mondini","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s10346-021-01785-4","article-title":"Deformation Responses of Landslides to Seasonal Rainfall Based on InSAR and Wavelet Analysis","volume":"19","author":"Liu","year":"2022","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geomorph.2014.11.031","article-title":"Landslide Deformation Monitoring with ALOS\/PALSAR Imagery: A D-InSAR Geomorphological Interpretation Method","volume":"231","author":"Doubre","year":"2015","journal-title":"Geomorphology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1109\/LGRS.2019.2918254","article-title":"Landslide Monitoring Using Change Detection in Multitemporal Optical Imagery","volume":"17","author":"Huang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5047","DOI":"10.1109\/JSTARS.2019.2951725","article-title":"Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains","volume":"12","author":"Ye","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-015-0165-0","article-title":"UAV Monitoring and Documentation of a Large Landslide","volume":"8","author":"Lindner","year":"2016","journal-title":"Appl. Geomat."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10346-018-0978-0","article-title":"Multitemporal UAV Surveys for Landslide Mapping and Characterization","volume":"15","author":"Rossi","year":"2018","journal-title":"Landslides"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40677-017-0073-1","article-title":"Spaceborne, UAV and Ground-Based Remote Sensing Techniques for Landslide Mapping, Monitoring and Early Warning","volume":"4","author":"Casagli","year":"2017","journal-title":"Geoenviron. Disasters"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s43020-023-00095-5","article-title":"GNSS Techniques for Real-Time Monitoring of Landslides: A Review","volume":"4","author":"Huang","year":"2023","journal-title":"Satell. Navig."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1080\/19475705.2014.889046","article-title":"Performance of Low-Cost GNSS Receiver for Landslides Monitoring: Test and Results","volume":"6","author":"Cina","year":"2015","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"\u0160egina, E., Peternel, T., Urban\u010di\u010d, T., Realini, E., Zupan, M., Je\u017e, J., Caldera, S., Gatti, A., Tagliaferro, G., and Consoli, A. (2020). Monitoring Surface Displacement of a Deep-Seated Landslide by a Low-Cost and near Real-Time GNSS System. Remote Sens., 12.","DOI":"10.3390\/rs12203375"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1007\/s10346-020-01408-4","article-title":"GB-InSAR Monitoring of Vegetated and Snow-Covered Slopes in Remote Mountainous Environments","volume":"17","author":"Woods","year":"2020","journal-title":"Landslides"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiao, T., Huang, W., Deng, Y., Tian, W., and Sha, Y. (2021). Long-Term and Emergency Monitoring of Zhongbao Landslide Using Space-Borne and Ground-Based InSAR. Remote Sens., 13.","DOI":"10.3390\/rs13081578"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s10346-011-0307-3","article-title":"Ruinon Landslide (Valfurva, Italy) Activity in Relation to Rainfall by Means of GBInSAR Monitoring","volume":"9","author":"Casagli","year":"2012","journal-title":"Landslides"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107803","DOI":"10.1016\/j.geomorph.2021.107803","article-title":"Dynamic Characterization of a Slow-Moving Landslide System\u2013Assessing the Challenges of Small Process Scales Utilizing Multi-Temporal TLS Data","volume":"389","author":"Stumvoll","year":"2021","journal-title":"Geomorphology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"012145","DOI":"10.1088\/1755-1315\/833\/1\/012145","article-title":"Digital Terrestrial Photogrammetry for a Dense Monitoring of the Surficial Displacements of a Landslide","volume":"833","author":"Brezzi","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1139\/cgj-2014-0051","article-title":"Comparison of Airborne Laser Scanning, Terrestrial Laser Scanning, and Terrestrial Photogrammetry for Mapping Differential Slope Change in Mountainous Terrain","volume":"52","author":"Lato","year":"2015","journal-title":"Can. Geotech. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"297","DOI":"10.5194\/isprsannals-II-5-297-2014","article-title":"Landslide Monitoring by Fixed-Base Terrestrial Stereo-Photogrammetry","volume":"2","author":"Roncella","year":"2014","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9183","DOI":"10.1029\/JB094iB07p09183","article-title":"Mapping Small Elevation Changes over Large Areas: Differential Radar Interferometry","volume":"94","author":"Gabriel","year":"1989","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11069-010-9634-2","article-title":"Use of LIDAR in Landslide Investigations: A Review","volume":"61","author":"Jaboyedoff","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_27","unstructured":"Kaplan, E.D., and Hegarty, C. (2017). Understanding GPS\/GNSS: Principles and Applications, Artech House. [3rd ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 Photogrammetry: A Low-Cost, Effective Tool for Geoscience Applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Luhmann, T., Robson, S., Kyle, S., and Boehm, J. (2023). Close-Range Photogrammetry and 3D Imaging, Walter de Gruyter GmbH & Co KG.","DOI":"10.1515\/9783111029672"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pajali\u0107, S., Perani\u0107, J., Maksimovi\u0107, S., \u010ceh, N., Jagodnik, V., and Arbanas, \u017d. (2021). Monitoring and Data Analysis in Small-Scale Landslide Physical Model. Appl. Sci., 11.","DOI":"10.3390\/app11115040"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"547","DOI":"10.14358\/PERS.82.7.547","article-title":"Measurement of Surface Changes in a Scaled-Down Landslide Model Using High-Speed Stereo Image Sequences","volume":"82","author":"Feng","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10661-024-12339-1","article-title":"Capabilities of Using UAVs and Close Range Photogrammetry to Determine Short-Term Soil Losses in Forest Road Cut Slopes in Semi-Arid Mountainous Areas","volume":"196","author":"Akduman","year":"2024","journal-title":"Environ. Monit. Assess."},{"key":"ref_33","first-page":"479","article-title":"3D Visualization of Landslide Based on Close-Range Photogrammetry","volume":"18","author":"Zhang","year":"2019","journal-title":"Instrum. Mes. Metrol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Romeo, S., Di Matteo, L., Kieffer, D.S., Tosi, G., Stoppini, A., and Radicioni, F. (2019). The Use of Gigapixel Photogrammetry for the Understanding of Landslide Processes in Alpine Terrain. Geosciences, 9.","DOI":"10.3390\/geosciences9020099"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.-M. (2016, January 27\u201330). Structure-from-Motion Revisited. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"N\u00fa\u00f1ez-Andr\u00e9s, M.A., Prades-Valls, A., Matas, G., Buill, F., and Lantada, N. (2023). New Approach for Photogrammetric Rock Slope Premonitory Movements Monitoring. Remote Sens., 15.","DOI":"10.3390\/rs15020293"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1007\/s10346-023-02121-8","article-title":"Evaluation of Rockfall Trends at a Sedimentary Rock Cut near Manitou Springs, Colorado, Using Daily Photogrammetric Monitoring","volume":"20","author":"Walton","year":"2023","journal-title":"Landslides"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Butcher, B., Walton, G., Kromer, R., Gonzales, E., Ticona, J., and Minaya, A. (2024). High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations. Remote Sens., 16.","DOI":"10.3390\/rs16010066"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109065","DOI":"10.1016\/j.geomorph.2024.109065","article-title":"A Cost-Effective Image-Based System for 3D Geomorphic Monitoring: An Application to Rockfalls","volume":"449","author":"Blanch","year":"2024","journal-title":"Geomorphology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.isprsjprs.2012.03.007","article-title":"Correlation of Multi-Temporal Ground-Based Optical Images for Landslide Monitoring: Application, Potential and Limitations","volume":"70","author":"Travelletti","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Calvetti, F., Cotecchia, F., Galli, A., and Jommi, C. (2020). Digital Terrestrial Stereo-Photogrammetry for Monitoring Landslide Displacements: A Case Study in Recoaro Terme (VI). Geotechnical Research for Land Protection and Development, Springer International Publishing.","DOI":"10.1007\/978-3-030-21359-6"},{"key":"ref_42","unstructured":"Ray, S., and Turi, R.H. (1999, January 27\u201329). Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation. Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT\u201999), Calcutta, India."},{"key":"ref_43","unstructured":"Chen, T.-W., Chen, Y.-L., and Chien, S.-Y. (2008, January 8\u201310). Fast Image Segmentation Based on K-Means Clustering with Histograms in HSV Color Space. Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, Cairns, QLD, Australia."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_45","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"012097","DOI":"10.1088\/1755-1315\/428\/1\/012097","article-title":"Improvement of MNIST Image Recognition Based on CNN","volume":"428","author":"Wang","year":"2020","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A Flexible New Technique for Camera Calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","first-page":"266","article-title":"Digital Camera Calibration Methods: Considerations and Comparisons","volume":"36","author":"Remondino","year":"2006","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1006\/cviu.1996.0037","article-title":"Iterative Pose Estimation Using Coplanar Feature Points","volume":"63","author":"Oberkampf","year":"1996","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Kuang, Y., Sugimoto, S., Astrom, K., and Okutomi, M. (2013, January 1\u20138). Revisiting the PnP Problem: A Fast, General and Optimal Solution. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.291"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02693799008941549","article-title":"Kriging: A Method of Interpolation for Geographical Information Systems","volume":"4","author":"Oliver","year":"1990","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/BF00977785","article-title":"Two Algorithms for Constructing a Delaunay Triangulation","volume":"9","author":"Lee","year":"1980","journal-title":"Int. J. Comput. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kimball, J.M., Bowman, E.T., Gray, J.M.N.T., and Take, W.A. (2024). Evaluation of Laboratory Methods to Quantify Particle Size Segregation Using Image Analysis in Landslide Flume Tests. Landslides.","DOI":"10.1007\/s10346-024-02375-w"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, C., Tang, Z., Fang, K., and Xu, W. (2024). Three-Dimensional Reconstruction and Deformation Identification of Slope Models Based on Structured Light Method. Sensors, 24.","DOI":"10.3390\/s24030794"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105008","DOI":"10.1016\/j.autcon.2023.105008","article-title":"Feature-Constrained Real-Time Simultaneous Monitoring of Monocular Vision Odometry for Bridge Bearing Displacement and Rotation","volume":"154","author":"Su","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"112049","DOI":"10.1016\/j.measurement.2022.112049","article-title":"Monocular Vision Pose Determination-Based Large Rigid-Body Docking Method","volume":"204","author":"Luo","year":"2022","journal-title":"Measurement"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0013-7952(99)00086-1","article-title":"Measurement of Landslide Displacements Using a Wire Extensometer","volume":"55","author":"Corominas","year":"2000","journal-title":"Eng. Geol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Scaioni, M., Feng, T., Lu, P., Qiao, G., Tong, X., Li, R., Barazzetti, L., Previtali, M., and Roncella, R. (2015). Close-Range Photogrammetric Techniques for Deformation Measurement: Applications to Landslides. Modern Technologies for Landslide Monitoring and Prediction, Springer.","DOI":"10.1007\/978-3-662-45931-7_2"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7103039","DOI":"10.1155\/2016\/7103039","article-title":"A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications","volume":"2016","author":"Ye","year":"2016","journal-title":"J. Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e2702","DOI":"10.1002\/stc.2702","article-title":"Computer Vision-Based Monitoring of the 3-D Structural Deformation of an Ancient Structure Induced by Shield Tunneling Construction","volume":"28","author":"Ye","year":"2021","journal-title":"Struct. Control. Health Monit."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s12145-017-0291-9","article-title":"Unmanned Aerial Vehicle Based Remote Sensing Method for Monitoring a Steep Mountainous Slope in the Three Gorges Reservoir, China","volume":"10","author":"Huang","year":"2017","journal-title":"Earth Sci. Inform."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s11069-009-9374-3","article-title":"The Role of Infiltration Processes in Steep Slope Stability of Pyroclastic Granular Soils: Laboratory and Numerical Investigation","volume":"52","author":"Damiano","year":"2010","journal-title":"Nat. Hazards"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2899","DOI":"10.1007\/s11440-021-01169-x","article-title":"Centrifuge Modelling of Rainfall-Induced Slope Failure in Variably Saturated Soil","volume":"16","author":"Wang","year":"2021","journal-title":"Acta Geotech."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"14148","DOI":"10.1109\/TITS.2022.3147770","article-title":"Monocular Visual Traffic Surveillance: A Review","volume":"23","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Gasperini, S., Morbitzer, N., Jung, H., Navab, N., and Tombari, F. (2023, January 1\u20136). Robust Monocular Depth Estimation under Challenging Conditions. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00751"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"126558","DOI":"10.1016\/j.neucom.2023.126558","article-title":"Multi-Camera Multi-Object Tracking: A Review of Current Trends and Future Advances","volume":"552","author":"Amosa","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1111\/phor.12456","article-title":"A Survey on Conventional and Learning-Based Methods for Multi-View Stereo","volume":"38","author":"Stathopoulou","year":"2023","journal-title":"Photogramm. Rec."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"9396","DOI":"10.1109\/TPAMI.2021.3126387","article-title":"Low-Light Image and Video Enhancement Using Deep Learning: A Survey","volume":"44","author":"Li","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wang, H., Xie, Q., Zhao, Q., and Meng, D. (2020, January 13\u201319). A Model-Driven Deep Neural Network for Single Image Rain Removal. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00317"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"117416","DOI":"10.1016\/j.engstruct.2023.117416","article-title":"A Target-Free Vision-Based Method for out-of-Plane Vibration Measurement Using Projection Speckle and Camera Self-Calibration Technology","volume":"303","author":"Lv","year":"2024","journal-title":"Eng. Struct."},{"key":"ref_72","first-page":"173","article-title":"Camera Vibration Correction of Stereo-DIC Systems for Field Measurements","volume":"Volume 12550","author":"Feng","year":"2023","journal-title":"Proceedings of the International Conference on Optical and Photonic Engineering (icOPEN 2022)"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:38:07Z","timestamp":1760114287000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,23]]},"references-count":72,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234380"],"URL":"https:\/\/doi.org\/10.3390\/rs16234380","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,23]]}}}