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- 摘要
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[Developments in Earth Surface Processes] Remote Sensing of Geomorphology Volume 23 || Terrestrial laser scanner applied to fluvial geomorphology
摘要： Measuring river geometry and its evolution through time has always been a cornerstone of fluvial geomorphology. While experimental and numerical modeling of fluvial dynamics has been central in understanding long-term dynamics and testing ideas, they remain simplified versions of complex natural systems and cannot necessarily include all relevant processes. Field measurements are thus central to our understanding of elementary processes such as sediment entrainment and deposition, bank erosion, bedrock incision as well as the macroscopic dynamics of river reaches such as channel bed accretion/erosion, bedforms mobility, and river meandering. It is therefore not surprising that fluvial geomorphologists have quickly embraced the use of terrestrial laser scanner (TLS) to study rivers (e.g., Heritage and Hetherington, 2007; Hodge et al., 2009a). TLS allows 3D digitization of fluvial environment in a dense (sub-cm), accurate (mm precision), and nearly exhaustive way (Fig. 1). The very large range of spatial scales covered is particularly impressive, from individual pebbles to km long river reaches (e.g., Brasington et al., 2012). Sub-cm accuracy also offers the possibility of detecting very subtle changes (Lague et al., 2013), a key attribute to measure slow processes such as bedrock abrasion (Beer et al., 2017). Given the recent emphasis on the role of riparian processes on fluvial processes, the ability to digitize vegetation in 3D in relation to channel morphology offers a unique perspective in biogeomorphology. However, many of the promises of TLS have not really been fulfilled, and the scientific potential of the TLS dataset remains often untapped. This is largely due to the challenging aspects surrounding the processing of TLS data which, to a large extent, also apply to structure from motion (SfM) surveys (Passalacqua et al., 2015). Three challenges, akin to typical Big Data issues can be identified as follows: 1. Data Complexity: TLS data are 3D data and nearly exhaustive. This makes for very rich data but also extremely complex to process as the relevant information (e.g., ground, grains, riverbanks, vegetation) must be detected prior to scientific analysis (Fig. 1). TLS data is also natively non-regularly sampled, with strong spatial variations in point density and requires processing methods that are more complex than for 2D raster-based data such as satellite imagery. 2. Data Volume: the latest generation of TLS instruments generates billions of points in a day. Manual processing cannot realistically be applied, and automatic processing methods are paramount. This requires good programing skills as well as a culture of machine learning and computer vision approaches that are not necessarily part of the training of geomorphologists and requires bridging the gap with computer sciences. 3. Data Incompleteness: despite the very large field of view of TLS sensors, the resulting 3D data do not sample the entire surface (Fig. 1). The ground-based viewpoint imparts missing data behind obstacles (grains of any size and vegetation) and the laser is generally fully absorbed by water resulting in the lack of bathymetric data, a strong limitation in river environments. Processing methods must account for this lack of information.
关键词： Terrestrial laser scanner,sediment transport,vegetation classification,bank erosion,3D digitization,point cloud processing,bedrock incision,fluvial geomorphology
更新于2025-09-23 15:19:57
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Extrinsic calibration and kinematic modelling of a laser line triangulation sensor integrated in an intelligent fixture with 3 degrees of freedom
摘要： This article presents the kinematic modelling for the extrinsic calibration of a laser line profile sensor integrated in an intelligent fixture. It aims to characterize the real 3D shape of flexible parts when they are clamped to ensure suitable stiffness for machining processes, aiding to define the machining path to improve the precision of the process. This tool consists of two linear axes and a rotary axis (3 DOF) which enables scanning the area of the part to be processed automatically. In order to carry out the accuracy evaluation of the intelligent fixture, some methods present in the state of the art have been considered and compared. Moreover, in order to design and identify the most suitable calibration procedure a previous simulation process is carried out based on sensitivity analysis. To complete the study, a test piece has been scanned with the intelligent fixture and compared with an external metrological frame employed as a ground truth. In addition, a characterization of the geometric performance of the fixture's linear actuators is carried out to check the geometric performance and its influence on the extrinsic calibration process accuracy. The results of this article show the importance of performing simulation processes in order to define the best measurement scenario for extrinsic calibration. Besides, it demonstrates the influence of the method used to perform extrinsic calibration in order to obtain good precision in the measures, where the geometric performance of the drives have a decisive influence.
关键词： Point cloud processing,Kinematic modelling,Kinematic calibration,Inverse problem,Triangulation sensor
更新于2025-09-12 10:27:22
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Community-scale multi-level post-hurricane damage assessment of residential buildings using multi-temporal airborne LiDAR data
摘要： Building damage assessment is a critical task following major hurricane events. Use of remotely sensed data to support building damage assessment is a logical choice considering the di?culty of gaining ground access to the impacted areas immediately after hurricane events. However, a remote sensing based damage assessment approach is often only capable of detecting severely damaged buildings. In this study, an airborne LiDAR based approach is proposed to assess multi-level hurricane damage at the community scale. In the proposed approach, building clusters are ?rst extracted using a density-based algorithm. A novel cluster matching algorithm is proposed to robustly match post-event and pre-event building clusters. Multiple features including roof area and volume, roof orientation, and roof shape are computed as building damage indicators. A hierarchical determination process is then employed to identify the extent of damage to each building object. The results of this study suggest that our proposed approach is capable of 1) recognizing building objects, 2) extracting damage features, and 3) characterizing the extent of damage to individual building properties.
关键词： Hurricane damage assessment,Point cloud processing,Geometric computing,Airborne LiDAR,Data clustering
更新于2025-09-10 09:29:36