Surface Approximation of Coastal Regions: Lr B-Spline for Detection of Deformation Pattern

Kermarrec, Gaël; Skytt, Vibeke; Dokken, Tor

doi:10.5194/isprs-annals-v-2-2022-119-2022

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These improvements show the potential of combining datasets from different sources and should support the increasing needs for predicting deformation based on TLS observations, potentially coupled with GNSS observations. Here, mathematical approximation of the surface, as proposed in Kermarrec et al [38] with locally refined B-splines, will strongly mitigate the problems linked with the huge data size.…”

Section: Monitoring With Terrestrial Laser Scanners and Gnssmentioning

confidence: 99%

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Montillet

Zhao

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Geodesy is the science of accurately measuring the topography of the earth (geometric shape and size), its orientation in space, and its gravity field. With the advances in our knowledge and technology, this scientific field has extended to the understanding of geodynamical phenomena such as crustal motion, tides, and polar motion. This Special Issue is dedicated to the recent advances in modelling geodetic time series recorded using various instruments. Due to the stochastic noise properties inherent in each of the time series, careful modelling is necessary in order to extract accurate geophysical information with realistic associated uncertainties (statistically sufficient). The analyzed data have been recorded with various space missions or ground-based instruments. It is impossible to be comprehensive in the vast and dynamic field that is Geodesy, particularly so-called “Environmental Geodesy”, which intends to understand the Earth’s geodynamics by monitoring any changes in our environment. This field has gained much attention in the past two decades due to the need by the international community to understand how climate change modifies our environment. Therefore, this Special Issue collects some articles which emphasize the recent development of specific algorithms or methodologies to study particular natural phenomena related to the geodynamics of the earth’s crust and climate change.

show abstract

Section: Monitoring With Terrestrial Laser Scanners and Gnssmentioning

confidence: 99%

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Montillet

Zhao

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The underlying application would be a deformation analysis from a nearcontinuous monitoring as in [11,48]. Here, distances can be easily computed based on the approximated surfaces, and mathematical spatiotemporal volumes can help visualizing changes by reducing data storage [49]. It is mandatory to extract only the ground for such applications in order not to approximate and compare the growth of the vegetation.…”

Section: Application: Surface Fittingmentioning

confidence: 99%

Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation

Kermarrec

Yang²,

Czerwonka‐Schröder

2022

Remote Sensing

View full text Add to dashboard Cite

Terrestrial laser scanners (TLS) are contact-free measuring sensors that record dense point clouds of objects or scenes by acquiring coordinates and an intensity value for each point. The point clouds are scattered and noisy. Performing a mathematical surface approximation instead of working directly on the point cloud is an efficient way to reduce the data storage and structure the point clouds by transforming “data” to “information”. Applications include rigorous statistical testing for deformation analysis within the context of landslide monitoring. In order to reach an optimal approximation, classification and segmentation algorithms can identify and remove inhomogeneous structures, such as trees or bushes, to obtain a smooth and accurate mathematical surface of the ground. In this contribution, we compare methods to perform the classification of TLS point clouds with the aim of guiding the reader through the existing algorithms. Besides the traditional point cloud filtering methods, we will analyze machine learning classification algorithms based on the manual extraction of point cloud features, and a deep learning approach with automatic extraction of features called PointNet++. We have intentionally chosen strategies easy to implement and understand so that our results are reproducible for similar point clouds. We show that each method has advantages and drawbacks, depending on user criteria, such as the computational time, the classification accuracy needed, whether manual extraction is performed or not, and if prior information is required. We highlight that filtering methods are advantageous for the application at hand and perform a mathematical surface approximation as an illustration. Accordingly, we have chosen locally refined B-splines, which were shown to provide an optimal and computationally manageable approximation of TLS point clouds.

show abstract

Surface Approximation of Coastal Regions: Lr B-Spline for Detection of Deformation Pattern

Cited by 2 publications

References 10 publications

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Recent Advances in Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring

Classification of Terrestrial Laser Scanner Point Clouds: A Comparison of Methods for Landslide Monitoring from Mathematical Surface Approximation

Contact Info

Product

Resources

About