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Abstract Surveys of roadways with Mobile Laser Scanning (MLS) are now being conducted on a regular basis by many transportation agencies to provide detailed geometric information to support a wide range of applications, including asset management. Most MLS systems provide intensity (return signal strength) data as a point attribute in georeferenced point clouds, which may be used to estimate retro -reflectivity of pavement markings for effective maintenance. Nevertheless, the extraction of pavement markings from mobile lidar data remains an open challenge, due to variable noise, degree of wear on the markings, and road conditions. This paper addresses these challenges, presenting a novel approach for efficient, reliable extraction of lane markings, including those that have been significantly worn. First, using the MLS trajectory information, the lidar data is discretized into smaller sections, and then transformed to the local coordinate system, such that the road surface is near-horizontal for reliable extraction on roads with significant grade. Subsequently, the road surface is extracted using the constrained Random Sampling and Consensus (RANSAC) algorithm and then rasterized into a 2D intensity image to apply image processing techniques, namely: image segmentation to separate the lane markings from the road pavement, and a morphological opening operation to remove small objects. However, the extracted lane markings are prone to over-segmentation, due to occlusions or worn portions caused by moving vehicles. To rectify this, topologically-similar lane markings are associated with each other by computing line parameters (i.e., orientation and distance from the origin), which enables the gaps to be filled among the associated lanes. Finally, the remaining incorrect lane markings are detected and removed through a noise filtering phase using Dip test statistics. Examples of the effectiveness and application of the methodology are shown for a variety of sites with stripes of variable condition to highlight the robustness of the approach. Using optimized parameter values, the algorithm achieved F1 scores of 89–97% when tested on a variety of datasets encompassing a wide range of road scene types. [ABSTRACT FROM AUTHOR]

Accurate assessments of forest resources rely on ground truth data that are collected via in-situ measurements, which are fundamental for all other statistical- and/or remote-sensing-based deductions on quantified forest attributes. The major bottleneck of the current in-situ observation system is that the data collection is time consuming, and, thus, limited in extent, which potentially biases any further inferences made. Consequently, conventional field-data-collection approaches can hardly keep pace with the coverage, scale and frequency required for contemporary and future forest inventories. In-situ measurements from mobile platforms seem to be a promising technique to solve this problem and are estimated at least 10 times faster than static techniques (e.g., terrestrial laser scanning, TLS) at the plot level. However, the mobile platforms are still at the very early stages of development, and it is unclear which three-dimensional (3D) forest measurements the mobile systems can provide and at what accuracy. This study presents a quantitative evaluation of the performance of mobile platforms in a variety of forest conditions and through a comparison with state-of-the-art static in-situ observations. Two mobile platforms were used to collect field data, where the same laser-scanning system was both mounted on top of a vehicle and wore by an operator. The static in-situ observation from TLS is used as a baseline for the evaluation. All point clouds involved were processed through the same processing chain and compared to conventional manual measurement. The evaluation results indicate that the mobile platforms can assess homogeneous forests as well as static observations, but they cannot yet assess heterogeneous forest as required by practical applications. The major challenge is twofold: mobile-data coverage and accuracy. Future research should focus on the robust registration techniques between strips, especially in complex forest conditions, since errors of data registration results in significant impacts on tree attributes estimation accuracy. In cases that the spatial inconstancy cannot be eliminated, attributes estimation in single strips, i.e., the multi-single-scan approach, is an alternative. Meanwhile, operator training deserves attention since the data quality from mobile platforms is partly determined by the operators’ selection of trajectory in the field. [ABSTRACT FROM AUTHOR]

Indoor building models are essential in many indoor applications. These models are composed of the primitives of the buildings, such as the ceilings, floors, walls, windows, and doors, but not the movable objects in the indoor spaces, such as furniture. This paper presents, for indoor environments, a novel semantic line framework-based modeling building method using backpacked laser scanning point cloud data. The proposed method first semantically labels the raw point clouds into the walls, ceiling, floor, and other objects. Then line structures are extracted from the labeled points to achieve an initial description of the building line framework. To optimize the detected line structures caused by furniture occlusion, a conditional Generative Adversarial Nets (cGAN) deep learning model is constructed. The line framework optimization model includes structure completion, extrusion removal, and regularization. The result of optimization is also derived from a quality evaluation of the point cloud. Thus, the data collection and building model representation become a united task-driven loop. The proposed method eventually outputs a semantic line framework model and provides a layout for the interior of the building. Experiments show that the proposed method effectively extracts the line framework from different indoor scenes. [ABSTRACT FROM AUTHOR]

Abstract The article proposes a new approach of coastal cliff monitoring which bases on a new comprehensive monitoring system, being a combination of mobile scanning from the sea with the geotechnical stability analysis. Mobile laser scanning is an innovative solution for 3D data collection which allows the monitored object geometry to be precisely measured, thus providing opportunities for series of analyses contributing to the development of an improved monitoring system. The authors present selected cliff profiles obtained from mobile scanning in the area of Jastrzebia Gora, complemented by relevant geotechnical analyses. The analysis of current cliff geometry is accompanied by calculations of hypothetical cliff profile changes resulting from possible action of nature forces. Finally, a comprehensive strategy for coastal cliff monitoring is proposed. [ABSTRACT FROM AUTHOR]

The aim of the research was to analyze the possibility of using mobile laser scanning systems to acquire information for production and/or updating of a basic map and to propose a no-reference index of this accuracy assessment. Point clouds have been analyzed in terms of content of interpretation and geometric potential. For this purpose, the accuracy of point clouds with a georeference assigned to the base map objects was examined. In order to conduct reference measurements, a geodetic network was designed and also additional static laser scanning data has been used. The analysis of mobile laser scanning (MLS) data accuracy was conducted with the use of 395 check points. In the paper, application of the total Error of Position of the base-map Objects acquired with the use of MLS was proposed. Research results were related to reference total station measurements. The resulting error values indicate the possibility to use an MLS point cloud in order to accurately determine coordinates for individual objects for the purposes of standard surveying studies, e.g. for updating some elements of the base map content. Nevertheless, acquiring MLS point clouds with satisfying accuracy not always is possible, unless specific resolution condition is fulfilled. The paper presents results of accuracy evaluation in different classes of base-map elements and objects. [ABSTRACT FROM AUTHOR]

This paper introduces a new urban point cloud dataset for automatic segmentation and classification acquired by mobile laser scanning (MLS). We describe how the dataset is obtained from acquisition to post-processing and labeling. This dataset can be used to train pointwise classification algorithms; however, given that a great attention has been paid to the split between the different objects, this dataset can also be used to train the detection and segmentation of objects. The dataset consists of around 2 k m of MLS point cloud acquired in two cities. The number of points and range of classes mean that it can be used to train deep-learning methods. In addition, we show some results of automatic segmentation and classification. The dataset is available at: http://caor-mines-paristech.fr/fr/paris-lille-3d-dataset/. [ABSTRACT FROM AUTHOR]

The article describes the method of calibration of the Riegl VMZ-400 mobile scanning system on a floating platform and an experiment aimed at conducting measurements from a previously calibrated instrument. The issue of factors affecting the quality of measurements was discussed. Mobile laser scanning is an increasingly popular measurement solution, but it is still innovative on a European scale. The use of a floating platform is unique on a national scale. The presented solution is the result of the work of the Gdańsk University of Technology team on the development of the measurement procedure for scanning land from the sea and the river. [ABSTRACT FROM AUTHOR]

In the framework of infrastructure analysis and maintenance in an urban environment, it is important to address the safety of every road user. This paper presents a methodology for the evaluation of several safety indicators on pedestrian crossing environments using geometric and radiometric information extracted from 3D point clouds collected by a Mobile Mapping System (MMS). The methodology is divided in four main modules which analyze the accessibility of the crossing area, the presence of traffic lights and traffic signs, and the visibility between a driver and a pedestrian on the proximities of a pedestrian crossing. The outputs of the analysis are exported to a Geographic Information System (GIS) where they are visualized and can be further processed in the context of city management. The methodology has been tested on approximately 30 pedestrian crossings in cluttered urban environments of two different cities. Results show that MMS are a valid mean to assess the safety of a specific urban environment, regarding its geometric conditions. Remarkable results are presented on traffic light classification, with a global F-score close to 95%. [ABSTRACT FROM AUTHOR]

In this study we have investigated why diameters of tree stems, which are approximately cylindrical, are often overestimated by mobile laser scanning. This paper analyzes the physical processes when using ground-based laser scanning that may contribute to a bias when estimating cylinder diameters using circle-fit methods. A laser scanner simulator was implemented and used to evaluate various properties, such as distance, cylinder diameter, and beam width of a laser scanner-cylinder system to find critical conditions. The simulation results suggest that a positive bias of the diameter estimation is expected. Furthermore, the bias follows a quadratic function of one parameter – the relative footprint, i.e., the fraction of the cylinder width illuminated by the laser beam. The quadratic signature opens up a possibility to construct a compensation model for the bias. [ABSTRACT FROM AUTHOR]

During the last few years, the acquisition of geometric information about objects by using a moving sensor platform has gained increasing popularity in the surveying community. A large number of companies offer vehicle-based mobile mapping systems, which usually contain a fast profile laser scanner, a high-precision inertial measurement unit, and geodetic global navigation satellite system (GNSS) receivers to directly georeference the laser scans and build a consistent point cloud of the object. In contrast, there is a growing number of companies offering small and lightweight unmanned aerial vehicles (UAVs), which automatically fly over the area of interest and create dense point clouds of the environment by using camera images and photogrammetric-processing software. In this case, the georeferencing is usually realized by ground control points. This contribution summarizes activities performed by the authors to bring both fields closer together by developing a small (11×10×5 cm) and lightweight (240-g) direct-georeferencing unit, which is able to provide accurate position (<5 cm) and orientation (<1°) information in real time. The authors describe the development of the sensor unit and the sensor-fusion algorithms, address the topics of calibration and accuracy evaluation, and provide an overview of different applications in which the unit has been already used. These include the high-resolution acquisition of crop-surface models by using UAV-based imagery, the direct georeferencing of an autonomously flying robot with a laser scanner and multiple cameras, and the generation of laser point clouds using a human-carried mobile mapping system. [ABSTRACT FROM AUTHOR]

• Novel multi-view based deep learning approach for colored point cloud completion. • Generation of dense point clouds with a variable number of points. • Preserving the genuine high-level detailedness of laser scanning measurements. • A novel late fusion-based technique for integrating the view-level features. • New public Benchmark containing both synthetic point clouds and real MLS data. In this paper, we introduce a novel multi view-based method for completing high-resolution 3D point clouds of partial object shapes obtained by mobile laser scanning (MLS) platforms. Our approach estimates both the geometry and color cues of the missing or incomplete object segments, by projecting the 3D input point cloud by multiple virtual cameras, and performing 2D inpainting in the image domains of the different views. In contrast to existing state-of-the-art methods, our method can generate point clouds consisting of a variable number of points, depending on the detailedness of the input measurement, which property highly facilitates the efficient processing of MLS data with inhomogeneous point density. For training and quantitative evaluation of the proposed method, we provide a new point cloud dataset that consists of both synthetic point clouds of four different street objects with accurate ground truth, and real MLS measurements of partially or fully scanned vehicles. The quantitative and qualitative experiments on the provided dataset demonstrate that our method surpasses state-of-the-art approaches in reconstructing the local fine geometric structures as well as in estimating the overall shape and color pattern of the objects. [ABSTRACT FROM AUTHOR]

Building accessibility diagnosis is of high interest especially in case of people with reduced mobility. This paper proposes a methodology for automated detection of inaccessible steps in building façade entrances from MLS (mobile laser scanner) data. Our approach uses the MLS trajectory to automatically subdivide urban point clouds into regular stretches. From each stretch, the lower zone of façade is isolated and selected as region of interest. Points belonging to vertical elements are projected onto a 2D image and steps are detected and classified as inaccessible areas according to the comparison of geometrical features such as height jump, proximity to ground and width, with regulation. The methodology has been tested in four real datasets, which constitute > 400 m of different urban scenarios. Results exhibit a robust performance under urban scenes with a high variability of façade geometry due to the presence of different entrance types to shops and dwellings. Results have been quantitatively evaluated and they show global F1 value around 93%. Moreover, the methodology is very fast since 100 m are processed in < 2 min. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) provides kinematic means to collect three dimensional data from surroundings for various mapping and environmental analysis purposes. Vehicle based MLS has been used for road and urban asset surveys for about a decade. The equipment to derive the trajectory information for the point cloud generation from the laser data is almost without exception based on GNSS-IMU (Global Navigation Satellite System – Inertial Measurement Unit) technique. That is because of the GNSS ability to maintain global accuracy, and IMU to produce the attitude information needed to orientate the laser scanning and imaging sensor data. However, there are known challenges in maintaining accurate positioning when GNSS signal is weak or even absent over long periods of time. The duration of the signal loss affects the severity of degradation of the positioning solution depending on the quality/performance level of the IMU in use. The situation could be improved to a certain extent with higher performance IMUs, but increasing system expenses make such approach unsustainable in general. Another way to tackle the problem is to attach additional sensors to the system to overcome the degrading position accuracy: such that observe features from the environment to solve for short term system movements accurately enough to prevent the IMU solution to drift. This results in more complex system integration with need for more calibration and synchronization of multiple sensors into an operational approach. In this paper we study operation of an ATV (All -terrain vehicle) mounted, GNSS-IMU based single scanner MLS system in boreal forest conditions. The data generated by RoamerR2 system is targeted for generating 3D terrain and tree maps for optimizing harvester operations and forest inventory purposes at individual tree level. We investigate a process-flow and propose a graph optimization based method which uses data from a single scanner MLS for correcting the post-processed GNSS-IMU trajectory for positional drift under mature boreal forest canopy conditions. The result shows that we can improve the internal conformity of the data significantly from 0.7 m to 1 cm based on tree stem feature location data. When the optimization result is compared to reference at plot level we reach down to 6 cm mean error in absolute tree stem locations. The approach can be generalized to any MLS point cloud data, and provides as such a remarkable contribution to harness MLS for practical forestry and high precision terrain and structural modeling in GNSS obstructed environments. [ABSTRACT FROM AUTHOR]

The standard method in surveying practice for adjusting mobile terrestrial laser scan (MTLS) data to correct for global navigation satellite system (GNSS) positioning errors consists of placing a detailed network of surveyed control targets along the corridor being scanned. Although this approach is easy to understand and implement, it is suboptimal with regard to accuracy, efficiency, and safety. Fortunately, the rapid data acquisition that characterizes MTLS enables the user to quickly obtain repeated independent measurements, providing a simple method to examine potential errors that may be present in an individual pass. These multiple passes can be statistically combined and input with GNSS and inertial measurement unit (IMU) data to allow adjustment of the vehicle trajectory. This paper investigates this new multipass approach (MPA) of collecting and processing MTLS data, which reduces control requirements, resulting in significant improvements in accuracy and economic and safety benefits. In this paper, two case studies using MPA are discussed, and the obtained data are analyzed to demonstrate the effectiveness of the MPA. In one of the case studies, GNSS conditions were poor; in the other case study, GNSS conditions were ideal. From these cases studies, the appropriate intervals for control targets were determined based on the desired level of accuracy. In both cases, the multipass approach was shown to be effective in significantly improving accuracy and reducing the need for dense control target networks. [ABSTRACT FROM AUTHOR]

This paper proposed an organization method of laser point cloud data for the efficient management and rapid visualization of the massive point cloud data of vehicle-mounted laser scanner. In this paper, both the original point cloud data and its trajectory are sectioned for the fast indexing firstly, then the LOD(levels of details) index of each section is build based on octree structure. With a tile type and multiresolution storage mode based on folder system, the depth of octree structure is represented by the level of folder directory, and in every node folder, the corresponding point cloud data file and its node properties file are both include. The storage method of this paper decreases the preprocessing time greatly and can support concurrent access owing to mutually independence of each node. Moreover, with the application of view frustum culling technology and multi-thread dynamic dispatch technology, the rendering and roaming of massive point cloud can realize real time updating according to viewpoint change, which significantly improves the dispatch efficiency of vehicle-mounted laser point cloud data. In the experiment, our method was compared with several popular software of point cloud data processing (e.g. XGRT, Quick Terrain Reader) on the data dispatch. The results show our method has obvious advantage on storage capacity, data access time, memory footprint, rendering frame rate and so on, it indicates our method is effective for the management of vehicle-mounted laser point cloud data and can improve the production efficiency for indoor-work of vehicle-mounted laser point cloud data processing. [ABSTRACT FROM AUTHOR]

Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-information products, such as digital terrain and elevation models and 3D building models, are produced automatically from these data. We outline the data acquisition, processing, and application of the point cloud. As a national data set, a dense multispectral point cloud could produce significant cost savings via improved automation in mapping and a reduction of overlapping surveying efforts. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA, FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research. [ABSTRACT FROM AUTHOR]

Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree. [ABSTRACT FROM AUTHOR]

The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The growing number of LiDAR scanning solutions offer an alternative to classical methods of measurement that are often used underground, the results obtained being comparable from an accuracy point of view but when work duration and degree of detail are taken into account, laser scanning is increasingly superior. Compared with other methods of remote sensing, the methods of laser scanning can be easier to use in order to perform subterranean measurements due to the fact that the fulfilment of lighting and visibility conditions is no longer necessary, only site access for crew and equipment being a prerequisite for mission execution. [ABSTRACT FROM AUTHOR]

Traffic signs are one of the most important safety elements in a road network. Particularly, road markings provide information about the limits and direction of each road lane, or warn the drivers about potential danger. The optimal condition of road markings contributes to a better road safety. Mobile Laser Scanning technology can be used for infrastructure inspection and specifically for traffic sign detection and inventory. This paper presents a methodology for the detection and semantic characterization of the most common road markings, namely pedestrian crossings and arrows. The 3D point cloud data acquired by a LYNX Mobile Mapper system is filtered in order to isolate reflective points in the road, and each single element is hierarchically classified using Neural Networks. State of the art results are obtained for the extraction and classification of the markings, with F-scores of 94% and 96% respectively. Finally, data from classified markings are exported to a GIS layer and maintenance criteria based on the aforementioned data are proposed. [ABSTRACT FROM AUTHOR]

The calibration and accuracy analysis of a novel, low-cost, adaptable mobile laser scanning (MLS) system using a Velodyne HDL-32E laser scanner and an Oxford Technical Solutions Inertial+2 inertial navigation system, is described. First, a static calibration of the laser scanner is discussed. The static calibration is shown to improve the overall relative accuracy of point cloud data from the scanner by approximately 20% over the manufacturer-supplied calibration. Then, the determination of system boresight angles and lever-arm offsets using a planar patch least-squares approach is presented. Finally, the calibrated and boresighted MLS is operated in a backpack mode to acquire multiple data sets in an area that contains dense ground control acquired using static terrestrial laser scanning (TLS) and a high-end, survey-grade MLS. The dense ground control is used to examine several methods of estimating the overall errors of the backpack MLS system. Detailed comparison of the MLS data with the TLS and survey-grade MLS control shows that, despite the system's low cost, it is able to reliably collect point cloud data with greater than 10-cm three-dimensional root-mean-square error accuracy. [ABSTRACT FROM AUTHOR]

• A piecewise distance effect function and the Blinn-Phong model-based angle effect correction is proposed to correct the raw intensity for road marking detection. • The background data-driven angle effect function (BDAF) is beneficial for enhancing marking and road classification. • The incidence angle span (IAS) has a major influence on intensity correction of road marking, the road marking separability was improved by 35%@535 pt/m2 when IAS was increased from 3.17° to 13.5°. The intensity data recorded by light detection and ranging (LiDAR) contains the spectral characteristics of a scanned target, making it an essential source of information in mobile laser scanning (MLS) related applications. However, the raw intensity can be distorted by distance and incidence angle, obstructing its use in autonomous driving. To correct the intensity distortion, an automatic demarcation selection, based on distance effect function, and a Blinn-Phong model-based angle effect correction, have been proposed to correct the raw intensity, establishing the relationship between intensity correction performance and the influencing factors, including modeling data selection, incidence angle span (IAS) and point cloud density. Separability (SEP) and balanced F score (BFS) were used to evaluate the separability, accuracy and totality of road marking detection. Experimental results showed that the background data-driven angle effect function (BDAF) used was beneficial for enhancing marking and road classification. IAS has a major influence on correcting intensity performance. SEP was improved by 35% when IAS was increased from 3.17° to 13.5°, with point density of 535 pt/m2. The IAS threshold could be adjusted according to the instrument performance used. It offers a meaningful investigation of detection and classification of planar target using corrected MLS intensity. [ABSTRACT FROM AUTHOR]

• The total leaf area (TLA) of the canopy was evaluated for variable-rate spraying. • The total grid area (TGA) of the canopy was measured by the first and second echoes of a 2D LiDAR. • The TGA-based TLA evaluation method can adapt to varying point density. In recent years, variable-rate spraying technology has been proposed to solve the pesticide waste and environmental pollution problems caused by traditional spraying methods. Variable-rate spraying methods use sensors to measure the leaf amounts in the target canopies, which are used to adjust the amount of liquid and ensure even spraying for canopies of different sizes and densities. One of these methods uses mobile laser scanning (MLS) technologies with a two-dimensional (2D) light detection and ranging (LiDAR) sensor to capture the point cloud of the canopy, and then the total point number (TPN) is used to evaluate the total leaf area (TLA) of the canopy as a basis for variable-rate spraying. However, the moving speed and ranging distance of LiDAR often vary in the process of spraying, resulting in an inconsistent TPN and a large influence on the accuracy of the TLA measurement. In this paper, a new method for estimating the TLA of an individual tree canopy is proposed based on the total grid area (TGA) measured from the first and second echoes of a LiDAR sensor. The grid area at a single point is calculated by multiplying the LiDAR moving resolution and scanning resolution. To evaluate the proposed method, we use an artificial tree, the branches of which are pruned to generate nine canopies with different densities. Both sides of the canopies are scanned at three distances, namely, 1.0 m, 1.5 m, and 2.0 m, and at a constant moving speed of 0.104 m/s. Then, the point cloud of each canopy is sampled multiple times to simulate a variety of moving speeds ranging from 1.040 m/s to 2.080 m/s. Finally, a total of 594 test canopies are obtained. Univariate polynomial regression is adopted to evaluate the TLA by using the TPN and TGA. When the polynomial order is set to 2, the coefficient of determination (R-squared) of the TPN evaluation is 0.5759 with a 4662.4542 cm2 root mean squared error (RMSE), and the R-squared of the TGA evaluation is 0.9617 with a 1401.1652 cm2 RMSE. The experimental results show that the proposed method can effectively eliminate the effects of different LiDAR moving speeds and ranging distances during TLA measurements. [ABSTRACT FROM AUTHOR]

Accurate road asphalt extent delineation is needed for road and street planning, road maintenance, and road safety assessment. In this article, a new approach for automatic roadside delineation is developed based on the line clouds concept. The method relies on line cloud grouping from point cloud laser data. Using geometric criteria, the initial 3D LiDAR point data is structured in lines covering the road surface. These lines are then grouped according to a set of quasi-planar restriction rules. Road asphalt edge limits are extracted from the end points of lines belonging to these groups. Finally a two-stage smoothing procedure is applied to correct for edge occlusions and other anomalies. The method was tested on a 2.1 km stretch of road, and the results were checked using a RTK-GNSS measured dataset as ground truth. Correctness and completeness were 99% and 97%, respectively. [ABSTRACT FROM AUTHOR]

The paper contains a survey of mobile scanning systems for measuring the railway clearance gauge. The research was completed as part of the project carried out for the PKP (PKP Polish Railway Lines S.A., Warsaw, Poland) in 2011-2013. The authors conducted experiments, including a search for the latest solutions relating to mobile measurement systems that meet the basic requirement. At the very least, these solutions needed to be accurate and have the ability for quick retrieval of data. In the paper, specifications and the characteristics of the component devices of the scanning systems are described. Based on experiments, the authors did some examination of the selected mobile systems to be applied for measuring the clearance gauge. The Riegl (VMX-250) and Z+F (Zoller + Fröhlich) Solution were tested. Additional test measurements were carried out within a 30-kilometer section of theWarsaw-Kraków route. These measurements were designed so as to provide various elements of the railway infrastructure, the track geometry and the installed geodetic control network. This ultimately made it possible to reduce the time for the preparation of geodetic reference measurements for the testing of the accuracy of the selected systems. Reference measurements included the use of the polar method to select profiles perpendicular to the axis of the track. In addition, the coordinates selected were well defined as measuring points of the objects of the infrastructure of the clearance gauge. All of the tested systems meet the accuracy requirements initially established (within the range of 2 cm as required by the PKP). The tested systems have shown their advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Decision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique. [ABSTRACT FROM AUTHOR]

The rapid development of scanning technology, especially mobile scanning, gives the possibility to collect spatial data coming from maritime measurement platforms and autonomous manned or unmanned vehicles. Presented solution is derived from the mobile scanning. However we should keep in mind that the specificity of laser scanning at sea and processing collected data should be in the form acceptable in Geographical Information Systems, especially typical for the maritime needs. At the same time we should be aware that data coming from maritime mobile scanning constitutes a new approach to the describing of maritime environment and brings a new perspective that is completely different than air and terrestrial scanning. Therefore, the authors, would like to present results of an experiment aimed at testing the possibilities of using mobile scanning at sea. Experiment was conducted in the harbour and the associated environment of neighbouring southern coast of the Baltic Sea. [ABSTRACT FROM AUTHOR]

Precise levelling is known for its accuracy and reliability in height determination, but the process itself is slow, laborious and expensive. We have started a project to study methods for height determination that could decrease the creation time of national height systems without losing the accuracy and reliability that is needed for them. In the pilot project described here, we study some of the alternative techniques with a pilot field test where we compared them with the precise levelling. The purpose of the test is not to evaluate the mutual superiority or suitability of the techniques, but to establish the background for a larger test and to find strong and weak points of each technique. The techniques chosen for this study were precise levelling, Mobile Laser Scanning (MLS) and Global Navigation Satellite System (GNSS) levelling, which included static Global Positioning System (GPS) and Virtual Reference Station (VRS) measurements. This research highlighted the differences of the studied techniques and gave insights about the framework and procedure for the later experiments. The research will continue in a larger scale, where the suitability of the techniques regarding the height systems is to be determined. [ABSTRACT FROM AUTHOR]

Vehicle-related ground occlusion is a common problem in MLS data. This study aims to design a detection and reconstruction method of static vehicle-related ground occlusion for MLS data. Ground extraction and vehicle segmentation are performed on the input point cloud data in advance. Then an α-shape boundary based on the prior vehicle geometry is designed to split non-ground empty area and ground occlusions. The occlusion is detected and matched with its corresponding vehicle using the relative position between them. This relative position relation and the height difference are used to detect the curb direction as the local road direction. Finally, the occlusions are reconstructed using two different methods: (1) a cell-based linear interpolation and (2) a point-based mathematical morphology. The methodology is tested by original scanned data and multi-temporal evaluation data captured from a residential area in Delft, the Netherlands with vehicle-mounted LiDAR sensors. The result shows that all occlusions cause by vehicles are successfully detected and the curb (road) direction is correctly extracted in most of the occluded areas. Both reconstructed results can visually integrate the original scanned data and recover the curb structure. The reconstruction errors of the linear interpolation method are 0.045 m in the z-axis direction and 0.051 m in total and the reconstruction errors of mathematical morphology are 0.048 m in the z-axis direction and 0.052 m in total. • Ground boundaries are detected with an α-shape based on prior knowledge of vehicle. • Occlusions and vehicles are matched with the relative positions between them. • The local road direction is accurately estimated by detecting the curb direction. • A fast linear interpolation method is designed to reconstruct occlusions. • A point-based mathematical morphology method is proposed for reconstructions. [ABSTRACT FROM AUTHOR]

• Tree diversity is positively related to canopy space occupation in manged forests. • The definition of the canopy space and tree size largely affects diversity-canopy space occupation relationships. • Diversity effects are strongest by considering the upper canopy space. • Novel conceptual framework to assess spatial crown complementarity. A more efficient use of limited canopy space and, thus, a higher canopy space occupation (CSO) in forests can result in an increased absorption of photosynthetically active radiation, which in turn can promote productivity. Although there is some evidence for a positive relationship between tree diversity (TD) and CSO, the generality of this hypothesis is still under debate. Here, we propose a conceptual framework that accounts for both the spatial complexity of canopy space and size-dependent interspecific tree interactions and tested it using mobile laser scanning data across larger spatial scales. We assessed the CSO at high resolution with two diversity indices, tree species richness (TSR) and the effective number of species (ENS) along a TSR gradient ranging from monocultures to 8-species mixtures in a mature and structurally complex mixed-species temperate forest. We found that the direction and strength of the TD-CSO relationship largely depended on the way how canopy space is defined and which tree size classes are considered to calculate TSR. Using an broad deliniation of canopy space no significant relationship between TD and CSO was evident. In contrast, when considering only the upper canopy space, a significant effect of TSR on CSO emerged. Importantly, the direction of this relationship was critically dependent on the tree size threshold underlying the TSR determination. For all trees with a diameter at breast height > 7 cm, we observed a significant negative relationship, while the opposite was the case when considering only large-sized trees. Our novel conceptual framework demonstrates that accurate estimation of canopy space complexity and tree size dependence is key to better understanding the processes underlying CSO. However, further information on diversity-canopy space occupation relationships from studies in different forests and forest types is needed. [ABSTRACT FROM AUTHOR]

Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario. [ABSTRACT FROM AUTHOR]

The need for better 3D documentation of the built environment has come to the fore in recent years, led primarily by city modelling at the large scale and Building Information Modelling (BIM) at the smaller scale. Automation is seen as desirable as it removes the time-consuming and therefore costly amount of human intervention in the process of model generation. BIM is the focus of this paper as not only is there a commercial need, as will be shown by the number of commercial solutions, but also wide research interest due to the aspiration of automated 3D models from both Geomatics and Computer Science communities. The aim is to go beyond the current labour-intensive tracing of the point cloud to an automated process that produces geometry that is both open and more verifiable. This work investigates what can be achieved today with automation through both literature review and by proposing a novel point cloud processing process. We present an automated workflow for the generation of BIM data from 3D point clouds. We also present quality indicators for reconstructed geometry elements and a framework in which to assess the quality of the reconstructed geometry against a reference. [ABSTRACT FROM AUTHOR]

Mobile mapping technology can be used to acquire geospatial data efficiently, accurately and at low cost, it is one of the most advanced technologies for geospatial data acquisition. A precondition for acquiring high quality point clouds using mobile laser systems is that the mobile system be well calibrated. Ground control points are used in the common method to calibrate mobile laser systems, but these methods as inflexible and it is labor intensive to collect ground control points. In this paper a mobile laser system calibration method using no ground control points is proposed, an actual system was calibrated using the proposed method, and its precision evaluated. [ABSTRACT FROM AUTHOR]

An automatic extraction method of expressway road surface from mobile laser scanning (MLS) data is presented. Through an analysis of the spatial characteristics of expressway MLS point cloud data, an approximate plane constraint (APC) method, an ordering least square method of slope estimation and multi-scale window iterative analysis (MWIA) method were developed to extract initial ground seed points on the grid, and then all road surface points are extracted from the original points based on local slope. Finally, two groups of actual point cloud data were selected for an experiment, the results show that the proposed method can quickly and accurately extract a road surface from MLS data. The correctness of our road extraction results was 95. 74% and completeness was 98. 11%. [ABSTRACT FROM AUTHOR]

Various field-based studies of meander development have been realised, yet fluviomorphological processes and their spatial patterns over bends are not fully understood. In this paper, the processes of three natural meander bends during a flood event are dissected using a unique study approach, which combines traditional field surveying, close-range remote sensing, and computational modelling. The combined study approach improves the spatial and temporal resolutions of the data compared to traditional survey methods, although the bathymetric data collection needs improvement. The analyses indicate that the influence of a flood on the morphological changes of point bars depends on the stage of the bend's development. The point bars may also experience lateral growth during low discharge. However, the point bar is too large a unit to find simplified relationships between the discharge and morphological changes. Instead, one should concentrate on different parts of the bend and the point bar. In general, the longer the inundation period of one part, the more probable that the area will experience net deposition. The magnitude of the sediment transport and the net morphological change are not interconnected: Areas of net erosion and deposition can experience either high or low sediment transport rates during a flood. The results are compared with earlier theories of meander development, which are also assessed. [ABSTRACT FROM AUTHOR]

In this paper, we describe the automatic extraction of centerlines of railroads. Mobile Laser Scanning systems are able to capture the 3D environment of the rail tracks with a high level of detail. Our approach first detects laser points that were reflected by the rail tracks, by making use of local properties such as parallelism and height in relation to neighboring objects. In the modeling stage, we present two approaches to determine the centerline location. The first approach generates center points in a data-driven manner by projecting rail track points to the parallel track, and taking the midpoint as initial center point. Next, a piecewise linear function is fitted through the center points to generate center points at a regular interval. The second approach models the rail track by fitting piecewise 3D track models to the rail track points. The model consists of a pair of two parallel rail tracks. The fitted pieces are smoothened by a Fourier series interpolation function. After that the centerline is implicitly determined by the geometric center of the pair of tracks. Reference data has been used to analyze the quality of our results, confirming that the position of the centerlines can be determined with an accuracy of 2-3 cm. [ABSTRACT FROM AUTHOR]

Integrated in a wide research assessing destabilizing and triggering factors to model cliff dynamic along the Dieppe's shoreline in High Normandy, this study aims at testing boat-based mobile LiDAR capabilities by scanning 3D point clouds of the unstable coastal cliffs. Two acquisition campaigns were performed in September 2012 and September 2013, scanning (1) a 30-km-long shoreline and (2) the same test cliffs in different environmental conditions and device settings. The potentials of collected data for 3D modelling, change detection and landslide monitoring were afterward assessed. By scanning during favourable meteorological and marine conditions and close to the coast, mobile LiDAR devices are able to quickly scan a long shoreline with median point spacing up to 10 cm. The acquired data are then sufficiently detailed to map geomorphological features smaller than 0.5 m. Furthermore, our capability to detect rockfalls and erosion deposits (>m) is confirmed, since using the classical approach of computing differences between sequential acquisitions reveals many cliff collapses between Pourville and Quiberville and only sparse changes between Dieppe and Belleville-sur-Mer. These different change rates result from different rockfall susceptibilities. Finally, we also confirmed the capability of the boat-based mobile LiDAR technique to monitor single large changes, characterizing the Dieppe landslide geometry with two main active scarps, retrogression up to 40 m and about 100,000 m of eroded materials. [ABSTRACT FROM AUTHOR]

Line segment detection in images is already a well-investigated topic, although it has received considerably less attention in 3D point clouds. Benefiting from current LiDAR devices, large-scale point clouds are becoming increasingly common. Most human-made objects have flat surfaces. Line segments that occur where pairs of planes intersect give important information regarding the geometric content of point clouds, which is especially useful for automatic building reconstruction and segmentation. This paper proposes a novel method that is capable of accurately extracting plane intersection line segments from large-scale raw scan points. The 3D line-support region, namely, a point set near a straight linear structure, is extracted simultaneously. The 3D line-support region is fitted by our Line-Segment-Half-Planes (LSHP) structure, which provides a geometric constraint for a line segment, making the line segment more reliable and accurate. We demonstrate our method on the point clouds of large-scale, complex, real-world scenes acquired by LiDAR devices. We also demonstrate the application of 3D line-support regions and their LSHP structures on urban scene abstraction. [ABSTRACT FROM AUTHOR]

Road roughness is the deviation of a road surface from a designed surface grade that influences safety conditions for road users. Mobile laser scanning ( MLS) systems provide a rapid, continuous and cost-effective way of collecting highly accurate and dense 3D point-cloud data along a route corridor. In this paper an algorithm for the automated estimation of road roughness from MLS data is presented, where a surface grid is fitted to the lidar points associated with the road surface. The elevation difference between the lidar points and their surface grid equivalents provides residual values in height which can be used to estimate roughness along the road surface. Tests validated the new road-roughness algorithm by successfully estimating surface conditions on multiple road sections. These findings contribute to a more comprehensive approach to surveying road networks. [ABSTRACT FROM AUTHOR]

Mobile Laser Scanning (MLS) systems are widely applied for spatial data collection and support applications in many aspects. In recent years, MLS technology had been introduced to railway applications and greatly enhanced the spatial detail and efficiency when compared to traditional approaches. However, the advance of MLS technology is not completely applied to railway environment. Typical MLS systems rely on integrated navigation through the use of Inertial Navigation Systems (INS) and Global Navigation Satellite Systems (GNSS) for geo-referencing, while operation under long-term GNSS outages or even GNSS-free environments, such as underground railway or long tunnels, remains a challenging issue due to the degraded operation of standalone inertial navigation. Commercial MLS systems usually employ high performance inertial measurement units (IMU) and various strategies to manage GNSS outages, but GNSS components are still necessary prior to and after experiencing the loss of GNSS signals. To tackle the problem of permanent GNSS outages, alternative methods are introduced to replace the GNSS and so allow the use of MLS systems in GNSS-free underground railway environments. Such approaches encourage the MLS systems to be developed into the Underground Railway Laser Scanning (URLS) systems, which may provide several alternative operational functions for the management of underground railway operation. [ABSTRACT FROM AUTHOR]

Point clouds collected in urban scenes contain a huge number of points (e.g., billions), numerous objects with significant size variability, complex and incomplete structures, and variable point densities, raising great challenges for the automated extraction of urban objects in the field of photogrammetry, computer vision, and robotics. This paper addresses these challenges by proposing an automated method to extract urban objects robustly and efficiently. The proposed method generates multi-scale supervoxels from 3D point clouds using the point attributes (e.g., colors, intensities) and spatial distances between points, and then segments the supervoxels rather than individual points by combining graph based segmentation with multiple cues (e.g., principal direction, colors) of the supervoxels. The proposed method defines a set of rules for merging segments into meaningful units according to types of urban objects and forms the semantic knowledge of urban objects for the classification of objects. Finally, the proposed method extracts and classifies urban objects in a hierarchical order ranked by the saliency of the segments. Experiments show that the proposed method is efficient and robust for extracting buildings, streetlamps, trees, telegraph poles, traffic signs, cars, and enclosures from mobile laser scanning (MLS) point clouds, with an overall accuracy of 92.3%. [ABSTRACT FROM AUTHOR]

The LiDAR (Light Detection And Ranging) technology is becoming a more and more popular method to collect spatial information. The acquisition of 3D data by means of one or several laser scanners mounted on a mobile platform (car) could quickly provide large volumes of dense data with centimeter-level accuracy. This is, therefore, the ideal solution to obtain information about objects with elongated shapes (corridors), and their surroundings. Point clouds used by specific applications must fulfill certain quality criteria, such as quantitative and qualitative indicators (i.e. precision, accuracy, density, completeness).Usually, the client fixes some parameter values that must be achieved. In terms of the precision, this parameter is well described, whereas in the case of density point clouds the discussion is still open. Due to the specificities of the MLS (Mobile Laser Scanning), the solution from ALS (Airborne Laser Scanning) cannot be directly applied. Hence, the density of the final point clouds, calculated as the number of points divided by "flat" surface area, is inappropriate. We present in this article three different ways of determining and interpreting point cloud density on three different test fields. The first method divides the number of points by the "flat" area, the second by the "three-dimensional" area, and the last one refers to a voxel approach. The most reliable method seems to be the voxel method, which in addition to the local density values also presents their spatial distribution. [ABSTRACT FROM AUTHOR]

Road markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The stem diameter distribution, stem form and quality information must be measured as accurately as possible to optimize cutting. For a detailed measurement of the stands, we developed and demonstrated the use of a multisource single-tree inventory (MS-STI). The two major bottlenecks in the current airborne laser scanning (ALS)-based single-tree-level inventory, tree detection and tree species recognition, are avoided in MS-STI. In addition to airborne 3D data, such as ALS, MS-STI requires an existing tree map with tree species information as the input information. In operational forest management, tree mapping would be carried out after or during the first thinning. It should be highlighted that the tree map is a challenging prerequisite, but that the recent development in mobile 2D and 3D laser scanning indicates that the solution is within reach. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System. Predictors for tree quality attributes were extracted from ALS data or digital stereo imagery (DSI) and used in the nearest-neighbor estimation approach. Stem distribution was compiled by summing the predicted single-tree measures. The accuracy of the MS-STI was validated using harvester data (timber assortments) and field measures (stem diameter, tree height). RMSEs for tree height, diameter, saw log volume and pulpwood volume varied from 4.2% to 5.3%, from 10.9% to 19.9%, from 28.7% to 43.5% and from 125.1% to 134.3%, respectively. Stand-level saw log recoveries differed from -2.2% to 1.3% from the harvester measurements, as the respective differences in pulpwood recovery were between -3.0% and 10.6%. We conclude that MS-STI improves the predictions of stem-diameter distributions and provides accurate estimates for tree quality variables if an accurate tree map is available. [ABSTRACT FROM AUTHOR]

This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution. [ABSTRACT FROM AUTHOR]

Highlights: [•] We develop an automatic method for the segmentation of tunnel luminaires. [•] We use colored 3D point clouds acquired with a mobile terrestrial LiDAR system. [•] A four-stage algorithm based on color and geometrical features is proposed. [•] This automated method increases productivity of tunnel inspection works. [ABSTRACT FROM AUTHOR]

Morphological changes and hydraulic parameters are analyzed over a meander point bar along a sand-bedded river. Morphological changes caused by a flood are mapped using terrestrial and mobile laser scanners. The flow characteristics are simulated over the duration of the flood, which inundated the point bar completely. The spatial distribution of depth-averaged flow velocity, direction, and stream power at two different discharges is analyzed. In addition, the temporal variation of flow velocity, depth, and stream power on the point bar is resolved over the whole inundated period. The changes in discharge control the transverse shift of the high velocity core, which, in turn, has a major impact on the changes over the point bar. For example, a minor decrease in discharge may lead to a notable decrease in flow velocity on the point bar margin, enabling filling. Deposition occurs mainly during the recession of the flood, and thus, the duration of moderate discharges greatly influences accretion. The size of the supplied sediment also influences the filling over the bar head. Erosion mainly takes place during high discharges. A rapid decrease in discharge inhibits chute filling. The latest flood event seems to influence more the erosional compared to depositional processes. [ABSTRACT FROM AUTHOR]

Accurate terrain models are a crucial component of studies of river channel evolution. In this paper we describe a new methodology for creating high-resolution seamless digital terrain models (DTM) of river channels and their floodplains. We combine mobile laser scanning and low-altitude unmanned aerial vehicle (UAV) photography-based methods for creating both a digital bathymetric model of the inundated river channel and a DTM of a point bar of a meandering sub-arctic river. We evaluate mobile laser scanning and UAV-based photogrammetry point clouds against terrestrial laser scanning and combine these data with an optical bathymetric model to create a seamless DTM of two different measurement periods. Using this multi-temporal seamless data, we calculate a DTM of difference that allows a change detection of the meander bend over a one-year period. [ABSTRACT FROM AUTHOR]

Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets. [ABSTRACT FROM AUTHOR]