{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:23:09Z","timestamp":1760145789630,"version":"build-2065373602"},"reference-count":65,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T00:00:00Z","timestamp":1725062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fondazione Cariverona","award":["11170","QH20230270"],"award-info":[{"award-number":["11170","QH20230270"]}]},{"name":"Veneto Region through the grant \u201cScientific Support for the Characterization of Hydrogeological Risk and the Evaluation of the Effectiveness of Interventions Related to the Landslide Phenomenon of Busa del Cristo in Perarolo di Cadore (BL) through the Development of Predictive Geo-Hydrological Models\u201d","award":["11170","QH20230270"],"award-info":[{"award-number":["11170","QH20230270"]}]},{"name":"The research project of Central South University","award":["11170","QH20230270"],"award-info":[{"award-number":["11170","QH20230270"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological surveys due to its low-cost, ease of use, and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide, we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a newly developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views, we can quickly detect the occurrence of collapse events, determine their location, and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we conducted preliminary tests of the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining the collapse information proposed in this paper can extend the potential of landslide monitoring systems based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.<\/jats:p>","DOI":"10.3390\/rs16173233","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"3233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhipeng","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Infrastructure, Second Xiangya Hospital, Central South University, Changsha 410011, China"}]},{"given":"Fabio","family":"Gabrieli","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Ognissanti 39, 35129 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6368-7194","authenticated-orcid":false,"given":"Antonio","family":"Pol","sequence":"additional","affiliation":[{"name":"IATE, University of Montpellier, INRAE, Institut Agro, 2 Place Pierre Viala, 34060 Montpellier, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1077-8874","authenticated-orcid":false,"given":"Lorenzo","family":"Brezzi","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Architectural Engineering, University of Padova, Via Ognissanti 39, 35129 Padova, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.enggeo.2009.03.004","article-title":"Close-Range Terrestrial Digital Photogrammetry and Terrestrial Laser Scanning for Discontinuity Characterization on Rock Cuts","volume":"106","author":"Sturzenegger","year":"2009","journal-title":"Eng. Geol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.05.008","article-title":"Surface Reconstruction and Landslide Displacement Measurements with Pl\u00e9iades Satellite Images","volume":"95","author":"Stumpf","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Livio, F.A., Bovo, F., Gabrieli, F., Gambillara, R., Rossato, S., Martin, S., and Michetti, A.M. (2022). Stability Analysis of a Landslide Scarp by Means of Virtual Outcrops: The Mt. Peron Niche Area (Masiere Di Vedana Rock Avalanche, Eastern Southern Alps). Front. Earth Sci., 10.","DOI":"10.3389\/feart.2022.863880"},{"key":"ref_4","unstructured":"Antonello, M., Gabrieli, F., Cola, S., and Menegatti, E. (2013, January 27\u201328). Automated Landslide Monitoring through a Low-Cost Stereo Vision System. Proceedings of the CEUR Workshop Proceedings, Paris, France."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.geomorph.2016.06.030","article-title":"A Low-Cost Landslide Displacement Activity Assessment from Time-Lapse Photogrammetry and Rainfall Data: Application to the Tessina Landslide Site","volume":"269","author":"Gabrieli","year":"2016","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Giacomini, A., Thoeni, K., Santise, M., Diotri, F., Booth, S., Fityus, S., and Roncella, R. (2020). Temporal-Spatial Frequency Rockfall Data from Open-Pit Highwalls Using a Low-Cost Monitoring System. Remote Sens., 12.","DOI":"10.3390\/rs12152459"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ding, A., Zhang, Q., Zhou, X., and Dai, B. (2016, January 11\u201313). Automatic Recognition of Landslide Based on CNN and Texture Change Detection. Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation, YAC, Wuhan, China.","DOI":"10.1109\/YAC.2016.7804935"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Meena, S.R., Blaschke, T., and Aryal, J. (2019). UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11172046"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lei, T., Zhang, Q., Xue, D., Chen, T., Meng, H., and Nandi, A.K. (2019, January 12\u201317). End-to-End Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682802"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/JSTARS.2018.2803784","article-title":"Landslide Inventory Mapping from Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation","volume":"11","author":"Lv","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Amit, S.N.K.B., and Aoki, Y. (2017, January 26\u201327). Disaster Detection from Aerial Imagery with Convolutional Neural Network. Proceedings of the Proceedings\u2014International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017, Surabaya, Indonesia.","DOI":"10.1109\/KCIC.2017.8228593"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ji, S., Shen, Y., Lu, M., and Zhang, Y. (2019). Building Instance Change Detection from Large-Scale Aerial Images Using Convolutional Neural Networks and Simulated Samples. Remote Sens., 11.","DOI":"10.3390\/rs11111343"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/LGRS.2010.2101045","article-title":"Object-Oriented Change Detection for Landslide Rapid Mapping","volume":"8","author":"Lu","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object Recognition from Local Scale-Invariant Features. Proceedings of the IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006, January 18\u201322). SURF: Speeded up Robust Features. Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Berlin, Germany.","DOI":"10.1007\/11744023_32"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Blanch, X., Abellan, A., and Guinau, M. (2020). Point Cloud Stacking: A Workflow to Enhance 3D Monitoring Capabilities Using Time-Lapse Cameras. Remote Sens., 12.","DOI":"10.3390\/rs12081240"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Blanch, X., Eltner, A., Guinau, M., and Abellan, A. (2021). Multi-Epoch and Multi-Imagery (Memi) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras. Remote Sens., 13.","DOI":"10.3390\/rs13081460"},{"key":"ref_23","first-page":"163","article-title":"Image Recognition of Green Weeds in Cotton Fields Based on Color Feature","volume":"25","author":"Shen","year":"2009","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1016\/j.patcog.2011.10.001","article-title":"Shadow Detection: A Survey and Comparative Evaluation of Recent Methods","volume":"45","author":"Sanin","year":"2012","journal-title":"Pattern Recognit"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, Z., Bovik, A., and Sheikh, H. (2005). Structural Similarity Based Image Quality Assessment. Digital Video Image Quality and Perceptual Coding, Ser. Series in Signal Processing and Communications, RC Press.","DOI":"10.1201\/9781420027822.ch7"},{"key":"ref_26","unstructured":"Lim, J.S. (1990). Two-Dimensional Signal and Image Processing, Prentice Hall Inc."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2015, January 7\u201312). Learning to Compare Image Patches via Convolutional Neural Networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1109\/TIP.2011.2173206","article-title":"On the Mathematical Properties of the Structural Similarity Index","volume":"21","author":"Brunet","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss Functions for Image Restoration with Neural Networks","volume":"3","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Wang, T. (2024). Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities. Remote Sens., 16.","DOI":"10.3390\/rs16081344"},{"key":"ref_31","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale Structural Similarity for Image Quality Assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TIP.2013.2251645","article-title":"Structural Texture Similarity Metrics for Image Analysis and Retrieval","volume":"22","author":"Zujovic","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.isprsjprs.2016.07.006","article-title":"Satellite Images Analysis for Shadow Detection and Building Height Estimation","volume":"119","author":"Liasis","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1080\/01431160701395302","article-title":"Shadow Detection in Colour High-Resolution Satellite Images","volume":"29","author":"Ambrosio","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2013.02.003","article-title":"Shadow Detection in Very High Spatial Resolution Aerial Images: A Comparative Study","volume":"80","author":"Adeline","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.rse.2012.06.018","article-title":"A Physics-Based Atmospheric and BRDF Correction for Landsat Data over Mountainous Terrain","volume":"124","author":"Li","year":"2012","journal-title":"Remote Sens. Env."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hua, S., and Shi, P. (2014, January 14\u201316). GrabCut Color Image Segmentation Based on Region of Interest. Proceedings of the 2014 7th International Congress on Image and Signal Processing, CISP 2014, Dalian, China.","DOI":"10.1109\/CISP.2014.7003812"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1109\/31.83870","article-title":"Center Weighted Median Filters and Their Applications to Image Enhancement","volume":"38","author":"Ko","year":"1991","journal-title":"IEEE Trans. Circuits Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/83.370679","article-title":"Adaptive Median Filters: New Algorithms and Results","volume":"4","author":"Hwang","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","first-page":"1","article-title":"Image Denoising Using New Adaptive Based Median Filter","volume":"5","author":"Shrestha","year":"2014","journal-title":"Signal Image Process"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TSMC.1984.6313341","article-title":"An Optimal Multiple Threshold Scheme for Image Segmentation","volume":"4","author":"Reddi","year":"1984","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_42","unstructured":"Deng, G., and Cahill, L.W. (November, January 31). An Adaptive Gaussian Filter for Noise Reduction and Edge Detection. Proceedings of the 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TCE.2005.1405723","article-title":"Block-Based Noise Estimation Using Adaptive Gaussian Filtering","volume":"51","author":"Shin","year":"2005","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.isprsjprs.2011.10.003","article-title":"3D Building Reconstruction Based on given Ground Plan Information and Surface Models Extracted from Spaceborne Imagery","volume":"67","author":"Tack","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhao, L., and Li, J. (2012, January 23\u201325). The Applications and Summary of Three Dimensional Reconstruction Based on Stereo Vision. Proceedings of the 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, Xi\u2019an, China.","DOI":"10.1109\/ICICEE.2012.168"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isprsjprs.2015.10.006","article-title":"Sensor Modelling and Camera Calibration for Close-Range Photogrammetry","volume":"115","author":"Luhmann","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","unstructured":"Heikkila, J., and Silven, O. (1997, January 17\u201319). Four-Step Camera Calibration Procedure with Implicit Image Correction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"63","DOI":"10.5194\/isprs-annals-IV-5-W1-63-2017","article-title":"Image-based reconstruction and analysis of dynamic scenes in a landslide simulation facility","volume":"4","author":"Scaioni","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kj\u00e6r-Nielsen, A., Jensen, L.B.W., S\u00f8Srensen, A.S., and Kr\u00fcger, N. (2008, January 3\u20135). A Real-Time Embedded System for Stereo Vision Preprocessing Using an FPGA. Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs, ReConFig 2008, Cancun, Mexico.","DOI":"10.1109\/ReConFig.2008.63"},{"key":"ref_52","first-page":"284","article-title":"FPGA-Based Lens Undistortion and Image Rectification for Stereo Vision Applications","volume":"Volume 11144","author":"Junger","year":"2019","journal-title":"Photonics and Education in Measurement Science"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2004). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8742920","DOI":"10.1155\/2016\/8742920","article-title":"Literature Survey on Stereo Vision Disparity Map Algorithms","volume":"2016","author":"Hamzah","year":"2016","journal-title":"J. Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.isprsjprs.2005.02.008","article-title":"A Layered Stereo Matching Algorithm Using Image Segmentation and Global Visibility Constraints","volume":"59","author":"Bleyer","year":"2005","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.micpro.2007.10.002","article-title":"Real-Time Disparity Map Computation Module","volume":"32","author":"Georgoulas","year":"2008","journal-title":"Microprocess. Microsyst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s00371-003-0207-1","article-title":"Automatic Surface Reconstruction with Alpha-Shape Method","volume":"19","author":"Xu","year":"2003","journal-title":"Vis. Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1080\/15481603.2017.1351148","article-title":"Automatic Estimation of Olive Tree Dendrometric Parameters Based on Airborne Laser Scanning Data Using Alpha-Shape and Principal Component Analysis","volume":"54","author":"Hadas","year":"2017","journal-title":"GIsci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Carrea, D., Abellan, A., Derron, M.H., Gauvin, N., and Jaboyedoff, M. (2021). Matlab Virtual Toolbox for Retrospective Rockfall Source Detection and Volume Estimation Using 3D Point Clouds: A Case Study of a Subalpine Molasse Cliff. Geosciences, 11.","DOI":"10.3390\/geosciences11020075"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Brezzi, L., Carraro, E., Pasa, D., Teza, G., Cola, S., and Galgaro, A. (2021). Post-Collapse Evolution of a Rapid Landslide from Sequential Analysis with FE and SPH-Based Models. Geosciences, 11.","DOI":"10.3390\/geosciences11090364"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Liu, Y., Brezzi, L., Liang, Z., Gabrieli, F., Zhou, Z., and Cola, S. (2024). Image Analysis and LSTM Methods for Forecasting Surficial Displacements of a Landslide Triggered by Snowfall and Rainfall. Landslides.","DOI":"10.1007\/s10346-024-02328-3"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Teza, G., Cola, S., Brezzi, L., and Galgaro, A. (2022). Wadenow: A Matlab Toolbox for Early Forecasting of the Velocity Trend of a Rainfall-Triggered Landslide by Means of Continuous Wavelet Transform and Deep Learning. Geosciences, 12.","DOI":"10.3390\/geosciences12050205"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Brezzi, L., Gabrieli, F., Vallisari, D., Carraro, E., Pol, A., Galgaro, A., and Cola, S. (2024). DIPHORM: An Innovative DIgital PHOtogrammetRic Monitoring Technique for Detecting Surficial Displacements of Landslides. Remote Sens., 16.","DOI":"10.3390\/rs16173199"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/36.739146","article-title":"Coherence Estimation for SAR Imagery","volume":"37","author":"Touzi","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Guccione, D.E., Turvey, E., Roncella, R., Thoeni, K., and Giacomini, A. (2024). Proficient Calibration Methodologies for Fixed Photogrammetric Monitoring Systems. Remote Sens., 16.","DOI":"10.3390\/rs16132281"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3233\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:46:20Z","timestamp":1760111180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,31]]},"references-count":65,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173233"],"URL":"https:\/\/doi.org\/10.3390\/rs16173233","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,8,31]]}}}