Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Christoph Holst
 -- 1367 2024-01-18 11:45:18 |
2 format change
Peter Tang
 Meta information modification 1367 2024-01-19 03:12:29 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Hosseini, K.; Reindl, L.; Raffl, L.; Wiedemann, W.; Holst, C. Methods for Measuring Landslides. Encyclopedia. Available online: https://encyclopedia.pub/entry/54043 (accessed on 16 November 2024).
Hosseini K, Reindl L, Raffl L, Wiedemann W, Holst C. Methods for Measuring Landslides. Encyclopedia. Available at: https://encyclopedia.pub/entry/54043. Accessed November 16, 2024.
Hosseini, Kourosh, Leonhard Reindl, Lukas Raffl, Wolfgang Wiedemann, Christoph Holst. "Methods for Measuring Landslides" Encyclopedia, https://encyclopedia.pub/entry/54043 (accessed November 16, 2024).
Hosseini, K., Reindl, L., Raffl, L., Wiedemann, W., & Holst, C. (2024, January 18). Methods for Measuring Landslides. In Encyclopedia. https://encyclopedia.pub/entry/54043
Hosseini, Kourosh, et al. "Methods for Measuring Landslides." Encyclopedia. Web. 18 January, 2024.
Methods for Measuring Landslides
Edit
This entry is adapted from the peer-reviewed paper 10.3390/rs16010138

Landslides are among the most destructive geo-disasters, causing substantial property damage and safety problems worldwide. Defined as the gravitational movement of mass down a slope, they can result from various events such as severe precipitation, earthquakes, volcanic activity, and human activities.

point cloud deformation analysis laser scanning surveying lidar geodesy

1. Introduction

Landslides are among the most destructive geo-disasters, causing substantial property damage and safety problems worldwide. Defined as the gravitational movement of mass down a slope [1], they can result from various events such as severe precipitation, earthquakes, volcanic activity, and human activities. These natural phenomena pose significant risks to infrastructure, including roads and buildings, and to human life. According to the World Health Organization, landslides affected an estimated 4.8 million people and caused more than 18,000 deaths between 1998 and 2017 [2].
Understanding and predicting landslides is pivotal for scientific inquiry. Researchers focus on studying their causes, mechanisms, and potential impacts to develop effective landslide prediction and early warning systems (LEWS) [3][4][5][6][7]. These systems are crucial for landslide management and risk mitigation. As we progress towards more technologically advanced methods of monitoring, high spatial resolution techniques have gained prominence. The following sections will delve into the significance and applications of these methods in contemporary landslide research.
One of the common approaches for landslide prediction and early warning is using numerical modeling and simulations, which allow for predicting the behavior of the landslide under different scenarios. This method is widely used to evaluate slopes’ stability and identify the critical factors that control landslide behavior [8][9][10][11].
This part of the research presents a comprehensive literature research of various methods for measuring landslides, including the Global Navigation Satellite System (GNSS), image-based monitoring, and TLS. Each method has its advantages and limitations, which will be discussed in detail.

2. Global Navigation Satellite System (GNSS)

GNSS sensors are one of the beneficial ways of monitoring landslides [12][13][14][15][16]. The GNSS technology has proved to be one of the most flexible and practical tools for monitoring purposes. These instruments enable precision at the centimeter level or even lower in static mode and lengthy observation times [17]. GNSS can offer precise and accurate data regarding the location and movement of landslides. By putting permanent GNSS receivers at strategic points on a landslide or in an area prone to landslides, it is possible to measure small-scale ground surface motions with high temporal resolution. GNSS may also be used to measure the velocity and acceleration of a landslide, which can provide important information about the landslide’s dynamics and possible risks and threats. The advances in GNSS technology have created new low-cost sensors that can provide continuous monitoring with medium to high precision, accuracy, and limited costs [17][18][19]. In this case, the precision of measuring is enough, but using these sensors for measuring the landslide provides low spatial resolution. Therefore, it is necessary to combine these sensors with other area-based methods to increase spatial resolution. It is possible to obtain a more comprehensive picture of a landslide and its development over time by combining GNSS data with the results of other remote sensing techniques, such as terrestrial laser scanning (TLS) and satellite images.
GNSS offers high precision in monitoring landslides, providing valuable data on location and movement. Its ability to measure velocities and accelerations is crucial for understanding landslide dynamics. However, its low spatial resolution necessitates integration with other methods for a comprehensive analysis.

3. Image-Based Monitoring

In addition to the accuracy needed to assess, with a given probability, the magnitude of the expected displacement and the number of other issues influence the choice of the best monitoring system to use. For instance, the size of the area to be controlled, the frequency of data gathering, the time it takes to provide the results, the stability of the reference system, and the impact of atmospheric conditions on measurement accuracy or operation [20].
Emerging Unmanned Aerial Vehicles (UAVs) are used for many purposes, including monitoring landslides. UAVs can provide high resolution compared to the first method. Furthermore, gathering data with them would be easier for large areas and regions that need to be monitored frequently during a short period. In most cases, UAVs use a camera to capture the images and make a 3D model from the area in each epoch [21][22][23][24][25]. With appropriate ground control, accuracies in the range of 3–10 cm in 3D can be expected, making UAV photogrammetry extremely appealing for monitoring applications [20][26]. Although most image-based methods in this field use airborne photogrammetry, some image-based research uses terrestrial systems [27][28][29]. Recent advancements in image-based methods have seen the rise of UAV-based Geographic Object-Based Image Analysis (GeoOBIA). This technique, utilizing high-resolution imagery from UAVs, allows for more detailed and frequent landslide assessments. A notable study in this area is the GeoBIA-based semi-automated landslide detection using UAV data [30], which demonstrates the potential of UAVs in enhancing spatial resolution and detection capabilities.
Satellite images are also used for monitoring landslides. Detecting and monitoring landslides can be accomplished in several different ways with the help of remote sensing methods, particularly satellite images. These methods have the potential to offer information regarding the location, extent, and movement of landslides, in addition to information regarding the dynamics and causes of landslides [31][32][33][34]. For instance, multispectral imagery, such as Landsat, can be applied to identify changes in vegetation cover and soil moisture, which can indicate the presence of a landslide, and radar imagery, such as that from the European Space Agency’s Sentinel-1 satellite, can be used to detect changes in the elevation of the ground surface caused by a landslide. There are also some disadvantages to this method when compared to point-based methods, and one of the primary concerns is the accuracy of the image-based method for landslide detection. As reported in [35], the accuracy ranged from 5 to 15 cm in several projects using different types of UAVs and cameras. In cases where landslides occur at a rate of several millimeters per year, the image-based method faces some serious challenges.
Image-based monitoring, bolstered by methods like UAV-based GeoOBIA, offers a unique perspective in landslide analysis, especially in identifying surface changes. While it faces challenges in accuracy, especially for slow-moving landslides, its integration with other methods can significantly improve overall monitoring effectiveness.

4. Terrestrial Laser Scanners

A fixed sensor known as a terrestrial laser scanner (TLS) automatically captures the range and angles in equally spaced scanning steps [36]. They use the laser’s flight time to compute distances to objects. Traditional measurement techniques do not have the advantages of three-dimensional laser scanning technology. It has high measurement accuracy and quick monitoring speed, can reflect the entire deformation trend of the landslide body, and can quickly produce high-precision, high-density three-dimensional point cloud data. It also does not need to touch the landslide body [37]. Consequently, numerous studies these days focus on using TLS for monitoring landslides [38][39][40][41][42][43]. However, this method also has some disadvantages, including a high volume of generated point clouds, especially for large areas, and the problem of accurately registering these point clouds. Based on this, there is some research about the reliability and accuracy of using TLS for monitoring [44][45]. In recent years, TLS and UAV photogrammetry have been used together to cover the disadvantages of using one of the methods alone [46][47][48][49][50].
The point clouds produced by laser scanners in two or more epochs are registered using different algorithms, such as ICP (Iterative closest point) [51] and M3C2 (multiscale model-to-model cloud comparison) [52], as well as their improved versions [53][54]. Feature matching is another method for matching the point clouds obtained from different epochs. This method is robust to noise in challenging conditions such as large-scale differences, and it can be used to register point clouds with a high degree of accuracy. This information can be used to monitor and evaluate the stability of places prone to landslides. Point cloud processing can provide vital information regarding potential landslide hazards, such as slope angles, surface roughness, and elevation changes, by examining the shape and structure of the terrain. This data can be used to construct detailed 3D models of the landscape and detect instability areas, which can aid in predicting and preventing landslides. In addition, point cloud analysis can be used to monitor changes in the terrain over time, which can aid in detecting early warning signals of landslides and tracking their advancement once they have occurred.

References

  1. Long, J.; Li, C.; Liu, Y.; Feng, P.; Zuo, Q. A Multi-Feature Fusion Transfer Learning Method for Displacement Prediction of Rainfall Reservoir-Induced Landslide with Step-like Deformation Characteristics. Eng. Geol. 2022, 297, 106494.
  2. Mizutori, M.; Guha-Sapir, D. Economic Losses, Poverty and Disasters 1998–2017; United Nations Office for Disaster Risk Reduction: Geneva, Switzerland, 2017; Volume 4, pp. 9–15.
  3. Yin, Y.; Liu, X.; Zhao, C.; Tomás, R.; Zhang, Q.; Lu, Z.; Li, B. Multi-Dimensional and Long-Term Time Series Monitoring and Early Warning of Landslide Hazard with Improved Cross-Platform SAR Offset Tracking Method. Sci. China Technol. Sci. 2022, 65, 1891–1912.
  4. Zhang, Y.; Tang, J.; Cheng, Y.; Huang, L.; Guo, F.; Yin, X.; Li, N. Prediction of Landslide Displacement with Dynamic Features Using Intelligent Approaches. Int. J. Min. Sci. Technol. 2022, 32, 539–549.
  5. Jiang, S. Study of Landslide Geological Hazard Prediction Method Based on Probability Migration. Nat. Hazards 2021, 108, 1753–1762.
  6. Ma, J.; Xia, D.; Guo, H.; Wang, Y.; Niu, X.; Liu, Z.; Jiang, S. Metaheuristic-Based Support Vector Regression for Landslide Displacement Prediction: A Comparative Study. Landslides 2022, 19, 2489–2511.
  7. Leinauer, J.; Weber, S.; Cicoira, A.; Beutel, J.; Krautblatter, M. Towards Prospective Failure Time Forecasting of Slope Failures. In Proceedings of the EGU General Assembly Conference Abstracts 2022, Vienna, Austria, 23–27 May 2022; p. EGU22-7673.
  8. Ma, P.; Cui, Y.; Wang, W.; Lin, H.; Zhang, Y. Coupling InSAR and Numerical Modeling for Characterizing Landslide Movements under Complex Loads in Urbanized Hillslopes. Landslides 2021, 18, 1611–1623.
  9. Tayyebi, S.M.; Pastor, M.; Stickle, M.M.; Yagüe, Á.; Manzanal, D.; Molinos, M.; Navas, P. SPH Numerical Modelling of Landslide Movements as Coupled Two-Phase Flows with a New Solution for the Interaction Term. Eur. J. Mech.-BFluids 2022, 96, 1–14.
  10. Heidarzadeh, M.; Ishibe, T.; Sandanbata, O.; Muhari, A.; Wijanarto, A.B. Numerical Modeling of the Subaerial Landslide Source of the 22 December 2018 Anak Krakatoa Volcanic Tsunami, Indonesia. Ocean Eng. 2020, 195, 106733.
  11. Xu, Y.; George, D.L.; Kim, J.; Lu, Z.; Riley, M.; Griffin, T.; de la Fuente, J. Landslide Monitoring and Runout Hazard Assessment by Integrating Multi-Source Remote Sensing and Numerical Models: An Application to the Gold Basin Landslide Complex, Northern Washington. Landslides 2021, 18, 1131–1141.
  12. Carlà, T.; Tofani, V.; Lombardi, L.; Raspini, F.; Bianchini, S.; Bertolo, D.; Thuegaz, P.; Casagli, N. Combination of GNSS, Satellite InSAR, and GBInSAR Remote Sensing Monitoring to Improve the Understanding of a Large Landslide in High Alpine Environment. Geomorphology 2019, 335, 62–75.
  13. Paziewski, J.; Sieradzki, R.; Baryla, R. Multi-GNSS High-Rate RTK, PPP and Novel Direct Phase Observation Processing Method: Application to Precise Dynamic Displacement Detection. Meas. Sci. Technol. 2018, 29, 035002.
  14. Melgar, D.; Melbourne, T.I.; Crowell, B.W.; Geng, J.; Szeliga, W.; Scrivner, C.; Santillan, M.; Goldberg, D.E. Real-Time High-Rate GNSS Displacements: Performance Demonstration during the 2019 Ridgecrest, California, Earthquakes. Seismol. Res. Lett. 2020, 91, 1943–1951.
  15. Capilla, R.M.; Berné, J.L.; Martín, A.; Rodrigo, R. Simulation Case Study of Deformations and Landslides Using Real-Time GNSS Precise Point Positioning Technique. Geomat. Nat. Hazards Risk 2016, 7, 1856–1873.
  16. Gümüş, K.; Selbesoğlu, M.O. Evaluation of NRTK GNSS Positioning Methods for Displacement Detection by a Newly Designed Displacement Monitoring System. Measurement 2019, 142, 131–137.
  17. Poluzzi, L.; Tavasci, L.; Corsini, F.; Barbarella, M.; Gandolfi, S. Low-Cost GNSS Sensors for Monitoring Applications. Appl. Geomat. 2020, 12, 35–44.
  18. Notti, D.; Cina, A.; Manzino, A.; Colombo, A.; Bendea, I.H.; Mollo, P.; Giordan, D. Low-Cost GNSS Solution for Continuous Monitoring of Slope Instabilities Applied to Madonna Del Sasso Sanctuary (NW Italy). Sensors 2020, 20, 289.
  19. Raffl, L.; Holst, C. Including Virtual Target Points from Laser Scanning into the Point-Wise Rigorous Deformation Analysis at Geo-Monitoring Applications. In Proceedings of the 5th Joint International Symposium on Deformation Monitoring-JISDM 2022, València, Spain, 20–22 June 2022; Editorial de la Universitat Politècnica de València: Valencia, Spain, 2022.
  20. Dall’Asta, E.; Forlani, G.; Roncella, R.; Santise, M.; Diotri, F.; Morra di Cella, U. Unmanned Aerial Systems and DSM Matching for Rock Glacier Monitoring. ISPRS J. Photogramm. Remote Sens. 2017, 127, 102–114.
  21. Eichel, J.; Draebing, D.; Kattenborn, T.; Senn, J.A.; Klingbeil, L.; Wieland, M.; Heinz, E. Unmanned Aerial Vehicle-based Mapping of Turf-banked Solifluction Lobe Movement and Its Relation to Material, Geomorphometric, Thermal and Vegetation Properties. Permafr. Periglac. Process. 2020, 31, 97–109.
  22. Cenni, N.; Fiaschi, S.; Fabris, M. Integrated Use of Archival Aerial Photogrammetry, GNSS, and InSAR Data for the Monitoring of the Patigno Landslide (Northern Apennines, Italy). Landslides 2021, 18, 2247–2263.
  23. Vanderhorst, H.R. Method of Applying Unmanned Aerial Vehicle (UAV) for Landslides Identification in the Dominican Republic. Preprint; In Review. 2022. Available online: https://www.researchsquare.com/article/rs-1653879/v1 (accessed on 17 July 2023).
  24. Đorđević, D.R.; Đurić, U.; Bakrač, S.T.; Drobnjak, S.M.; Radojčić, S. Using Historical Aerial Photography in Landslide Monitoring: Umka Case Study, Serbia. Land 2022, 11, 2282.
  25. Lian, X.; Li, Z.; Yuan, H.; Liu, J.; Zhang, Y.; Liu, X.; Wu, Y. Rapid Identification of Landslide, Collapse and Crack Based on Low-Altitude Remote Sensing Image of UAV. J. Mt. Sci. 2020, 17, 2915–2928.
  26. Peppa, M.V.; Mills, J.P.; Moore, P.; Miller, P.E.; Chambers, J.E. Accuracy assessment of a uav-based landslide monitoring system. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B5, 2016 XXIII ISPRS Congress, Prague, Czech Republic, 12–19 July 2016; pp. 895–902.
  27. Roncella, R.; Forlani, G.; Fornari, M.; Diotri, F. Landslide Monitoring by Fixed-Base Terrestrial Stereo-Photogrammetry. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2014, II–5, 297–304.
  28. Cardenal, J.; Mata, E.; Perez-Garcia, J.L.; Delgado, J.; Hernandez, M.A.; Gonzalez, A.; Diaz-de-Teren, J.R. Close Range Digital Photogrammetry Techniques Applied to Landslide Monitoring. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 37, 235–240.
  29. Lucks, L.; Hirt, P.-R.; Hoegner, L.; Stilla, U. Photogrammetric Monitoring of Gravitational Mass Movements in Alpine Regions by Markerless 3D Motion Capture. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, XLIII-B2-2022, 1063–1069.
  30. Kundal, S.; Chowdhury, A.; Bhardwaj, A.; Garg, P.K.; Mishra, V. GeoBIA-Based Semi-Automated Landslide Detection Using UAS Data: A Case Study of Uttarakhand Himalayas. In Proceedings of the SPIE Conferences, Boston, MA, USA, 18–19 April 2023; Volume 12327, pp. 321–329.
  31. Edrich, A.-K.; Yildiz, A.; Roscher, R.; Kowalski, J. A Modular and Scalable Workflow for Data-Driven Modelling of Shallow Landslide Susceptibility. In Proceedings of the EGU General Assembly Conference Abstracts 2022, Vienna, Austria, 23–27 May 2022; p. EGU22-4900.
  32. Ullo, S.L.; Langenkamp, M.S.; Oikarinen, T.P.; Del Rosso, M.P.; Sebastianelli, A.; Piccirillo, F.; Sica, S. Landslide Geohazard Assessment with Convolutional Neural Networks Using Sentinel-2 Imagery Data. In Proceedings of the IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium; IEEE: Yokohama, Japan, 2019; pp. 9646–9649.
  33. König, T.; Kux, H.J.H.; Mendes, R.M. Shalstab Mathematical Model and WorldView-2 Satellite Images to Identification of Landslide-Susceptible Areas. Nat. Hazards 2019, 97, 1127–1149.
  34. Wasowski, J.; Bovenga, F. Remote Sensing of Landslide Motion with Emphasis on Satellite Multi-Temporal Interferometry Applications. In Landslide Hazards, Risks, and Disasters; Elsevier: Amsterdam, The Netherlands, 2022; pp. 365–438.
  35. Hussain, Y.; Schlögel, R.; Innocenti, A.; Hamza, O.; Iannucci, R.; Martino, S.; Havenith, H.-B. Review on the Geophysical and UAV-Based Methods Applied to Landslides. Remote Sens. 2022, 14, 4564.
  36. Kermarrec, G.; Yang, Z.; Czerwonka-Schröder, D. Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation. Remote Sens. 2022, 14, 5099.
  37. Jiang, S.; Deng, X.; Chen, M. 3-D Laser Scanning Landslide Deformation Monitoring and Data Processing Based on Computer Cluster. J. Phys. Conf. Ser. 2019, 1345, 062039.
  38. Pesántez Cabrera, P.C. Land Movement Detection from Terrestrial Laser Scanner (LiDAR) Analysis. In Laser Radar Technology and Applications XXVI; Turner, M.D., Kamerman, G.W., Eds.; SPIE: Online, USA, 2021; p. 17.
  39. Ozdogan, M.V. Landslide Detection and Characterization Using Terrestrial 3D Laser Scanning (LiDAR). Acta Geodyn. Geomater. 2019, 16, 379–392.
  40. Zhao, L.; Ma, X.; Xiang, Z.; Zhang, S.; Hu, C.; Zhou, Y.; Chen, G. Landslide Deformation Extraction from Terrestrial Laser Scanning Data with Weighted Least Squares Regularization Iteration Solution. Remote Sens. 2022, 14, 2897.
  41. Guo, Y.; Li, X.; Ju, S.; Lyu, Q.; Liu, T. Utilization of 3D Laser Scanning for Stability Evaluation and Deformation Monitoring of Landslides. J. Environ. Public Health 2022.
  42. Pesántez, P.C. Landslide Study Using Terrestrial Laser Scanner (Lidar) Analysis. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, XLIII-B3-2020, 1251–1256.
  43. Jaboyedoff, M.; Derron, M.-H. Landslide Analysis Using Laser Scanners. In Developments in Earth Surface Processes; Elsevier: Amsterdam, The Netherlands, 2020; Volume 23, pp. 207–230.
  44. Abbas, M.A.; Fuad, N.A.; Idris, K.M.; Opaluwa, Y.D.; Hashim, N.M.; Majid, Z.; Sulaiman, S.A. Reliability of Terrestrial Laser Scanner Measurement in Slope Monitoring. IOP Conf. Ser. Earth Environ. Sci. 2019, 385, 012042.
  45. Zeybek, M.; Şanlıoğlu, İ. Accurate Determination of the Taşkent (Konya, Turkey) Landslide Using a Long-Range Terrestrial Laser Scanner. Bull. Eng. Geol. Environ. 2015, 74, 61–76.
  46. Boyd, J.; Chambers, J.; Wilkinson, P.; Peppa, M.; Watlet, A.; Kirkham, M.; Jones, L.; Swift, R.; Ulhemann, S.; Holmes, J.; et al. Coupling Terrestrial Laser Scanning and UAV Photogrammetry with Geoelectrical Data for Better Time-Lapse Hydrological Characterisation of an Active Landslide. In Proceedings of the EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022.
  47. Ji, H.; Luo, X. 3D Scene Reconstruction of Landslide Topography Based on Data Fusion between Laser Point Cloud and UAV Image. Environ. Earth Sci. 2019, 78, 534.
  48. Zheng, X.; Yang, X.; Ma, H.; Ren, G.; Yu, Z.; Yang, F.; Zhang, H.; Gao, W. Integrative Landslide Emergency Monitoring Scheme Based on GB-INSAR Interferometry, Terrestrial Laser Scanning and UAV Photography. J. Phys. Conf. Ser. 2019, 1213, 052069.
  49. Jiang, N.; Li, H.; Hu, Y.; Zhang, J.; Dai, W.; Li, C.; Zhou, J.-W. A Monitoring Method Integrating Terrestrial Laser Scanning and Unmanned Aerial Vehicles for Different Landslide Deformation Patterns. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 10242–10255.
  50. Jiang, N.; Li, H.-B.; Li, C.-J.; Xiao, H.-X.; Zhou, J.-W. A Fusion Method Using Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry for Landslide Deformation Monitoring Under Complex Terrain Conditions. IEEE Trans. Geosci. Remote Sens. 2022, 60, 4707214.
  51. Besl, P.J.; McKay, N.D. Method for Registration of 3-D Shapes. In Proceedings of the SPIE Conferences, Boston, MA, USA, 14–15 November 1991; pp. 586–606.
  52. Lague, D.; Brodu, N.; Leroux, J. Accurate 3D Comparison of Complex Topography with Terrestrial Laser Scanner: Application to the Rangitikei Canyon (N-Z). ISPRS J. Photogramm. Remote Sens. 2013, 82, 10–26.
  53. Winiwarter, L.; Anders, K.; Höfle, B. M3C2-EP: Pushing the Limits of 3D Topographic Point Cloud Change Detection by Error Propagation. ISPRS J. Photogramm. Remote Sens. 2021, 178, 240–258.
  54. Yang, Y.; Schwieger, V. Supervoxel-Based Targetless Registration and Identification of Stable Areas for Deformed Point Clouds. J. Appl. Geod. 2022, 17, 161–170.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Remote Sensing
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Kourosh Hosseini
,
Leonhard Reindl
,
Lukas Raffl
,
Wolfgang Wiedemann
,
Christoph Holst
View Times: 390
Revisions: 2 times (View History)
Update Date: 19 Jan 2024
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback