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Monitoring tree growth processes is relevant for ecological research and understanding the intricate relationship between vegetation and the environment. Time series analyses have revealed a correlation between leaf emergence timing and climate change, with earlier leaf emergence attributed to global warming. While traditional forest inventory methods struggle to quantify growth processes on small scales, terrestrial laser scanning provides a powerful alternative for providing high-resolution 3D information. This study explores the use of high-frequency hyper-temporal terrestrial laser scanning data to quantitatively describe deciduous tree growth, tested on a pedunculate oak (Quercus robur). The research aims to address key questions about detecting leaf growth in hypertemporal terrestrial laser scanning data. Additionally, it explores how 3D tree parameters and point cloud comparisons capture leaf and tree growth throughout the year. Results from M3C2 point cloud analyses indicate that the temporary branch movements correlate with precipitation. Over the year, branch movements were detected to increase with growing distance from the trunk.

A common problem when imaging and measuring through moving water surfaces is the quasi-random refraction caused by waves. The article presents two strategies to overcome this problem by lowering the complexity down to a planer air/water interface problem. In general, the methods assume that the shape of the water surface changes randomly over time and that the water surface moves around an idle-state (calm planar water surface). Thus, moments at which the surface normal is orientated vertically should occur more frequently than others should. By analysing a sequence of images taken from a stable camera position these moments could be identified – this can be done in the image or object space. It will be shown, that a simple median filtering of grey values in each pixel position can provide a corrected image freed from wave and glint effects. This should have the geometry of an image taken through calm water surface. However, in case of multi camera setups, the problem can be analysed in object space. By tracking homological underwater features, sets of image rays hitting accidently horizontal orientated water surface areas can be identified. Both methods are described in depth and evaluated on real and simulated data.

LiDAR bathymetry provides an efficient and comprehensive way to capture the topography of water bodies in shallow water areas. However, the penetration depth of this measurement method into the water column is limited by the medium water and water turbidity, resulting in a limited detectability of the bottom topography in deeper waters. An increase of the analyzable water depth is possible by the use of extended evaluation methods, in detail full-waveform stacking methods. So far, however, this has only been investigated for water depths of up to 3.50 m due to water turbidity. In this article, the potential of these extended data processing methods is investigated on an alpine mountain lake with low water turbidity and thus high analyzable water depth. Compared to the standard data processing, the penetration depth could be significantly increased by 58%. In addition, methods for depth-resolved water turbidity parameter determination on the basis of LiDAR bathymetry data were successfully tested.

If multimedia-photogrammetry is used for the generation of point clouds of submerged objects or of the water bottom, Snell’s law has to be considered. When the images are taken from air, image rays are refracted at the air-water interface. This results in the collinearity equations being no longer valid. Bundle block adjustment can still be solved by adding additional terms considering Snell’s law. Existing approaches usually assume that the water surface is flat. Refractive indices and water height can either be measured separately or included as unknowns in the adjustment. However, when the water surface is not flat due to the presence of waves, assuming a planar water surface leads to large geometric errors. This work will analyze the significance of those errors and propose a way of including water surface parameters as unknowns into the bundle block adjustment, both based on simulated data. The simulation reproduces multiple images taken simultaneously, e.g. from synchronized UAV cameras or from cameras on tripods.

Bridge structures can be surveyed using a number of different methods. Established are image-based methods using structure from motion by an unmanned aerial vehicle (UAV), terrestrial laser scanning (TLS), or a combination of both methods. Beyond static terrestrial laser scanning, buildings can also be efficiently surveyed using personal laser scanner (PLS) systems. The advantage here is the greater flexibility and increased speed compared to the static method. On the other hand, the accuracy may be more critical, and the resulting point cloud will be more sensitive to systematic global or local deformations under unfavorable measurement conditions. For example, temporary influences can lead to local mapping errors. These include influences such as uneven measurement system motion or non-static, feature-sparse environments. This study investigates the acquisition of 3D point clouds representing the outer shell of a concrete bridge using a PLS system. We demonstrate a method for detecting possible deformations in PLS point clouds using the example of a bridge structure. For this purpose, the reference (TLS) and the PLS point clouds are segmented into individual clusters and a segment-based ICP fine registration is performed. Different RMSE values for the upper road section (0.061 m) and for the pillar segments (0.021 m) as well as different transformation parameters indicate slight displacements in the PLS point cloud. The analysis of the cloud-to-cloud distances showed that there were slight deformations in the Z direction in the area of the road surface. In the lateral direction, no significant residual deviations were found in the area of the bridge pillars.

The presented research investigates different strategies to acquire high-precision digital elevation models (DEMs) of complex and inaccessible terrain using Structure-from-Motion and Multi-View Stereo applied to data of an unoccupied aerial system (UAS) equipped with real-time-kinematic (RTK)-GNSS. The survey scenarios are taken from real-life situations and thus, in comparison to many previous studies, provide information on how to operate under challenging conditions in difficult terrain. Among others, the study examines the influence of different flight configurations (parallel axes and cross-grid), flight altitudes (relative to ellipsoid or terrain) and associated variations in ground sampling distance, image orientations (nadir and oblique), advanced camera self-calibration techniques and georeferencing strategies in image block processing (direct and integrated) on the overall accuracy of the resulting DEMs. Random and systematic errors, including spatial patterns such as doming and bowling, are quantified using check points and differences between DEM calculations and independently acquired surface data from laser scans. This comprehensive analysis contributes valuable insights for UAS-based analysis of complex terrain with improved accuracy in DEM generation and subsequent applications like change detection.

Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. This paper contributes to keypoint-based registration research through variations of the standard workflow proposed in the literature, applying a two-staged strategy of global and local keypoint matching as well as prototypical keypoint projection and fine registration based on ICP. Further, by testing the utilized detector and descriptor on unstructured, multi-temporal and multi-source point clouds with variations in point cloud density, generalization ability is tested outside benchmark data. Therefore, data of the Bøverbreen glacier in Jotunheimen, Norway has been acquired in 2022 and 2023, deploying UAV-based image matching and terrestrial laser scanning. The results show good performance of the implemented robust matching algorithm PROSAC, requiring fewer iterations than the well-known RANSAC approach, but solving the rigid body transformation with TEASER++ is faster and more robust to outliers without demanding pre-knowledge of the data. Further, the results identify the keypoint detection as most limiting factor in speed and accuracy. Summarizing, keypoint-based coarse registration on low density point clouds, applying a global and local matching strategy and transformation estimation using TEASER++ is recommended. Keypoint projection shows potential, increasing number and precision in low density clouds, but has to be more robust. Further research needs to be carried out, focusing on identifying a fast and robust keypoint detector.

This study investigates the performance of eight different deep learning architectures for voxel segmentation in volume images. The motivation is to segment carbon in carbon reinforced concrete (CRC) in micro-tomography (μ-CT) data. Although there are many 3D convolutional neural networks (CNNs) available, it is not yet clear which one works best for these specific tasks. In this study, the authors compare the following networks: DenseVoxNet, HighResNet, Med3D, Residual 3D U-Net, 3D SkipDenseSeg, 3D U-Net, V-Net, and LV-Net. To provide a more general recommendation for selecting a neural network, three medical datasets were added in addition to the three CRC datasets to facilitate the selection of an appropriate network based on the dataset characteristics. The experiments emphasize the importance of the initial random state, used for example to initialize the network weights. On average, the 3D U-Net is the best generalizing CNN, followed by the Residual 3D U-Net and the 3D SkipDenseSeg. While the 3D U-Net is a good architecture to start with, the experiments show that it does not perform best on all domains. To achieve optimal results, the authors propose recommendations for selecting a 3D neural network based on the dataset attributes.

As an extension to existing work on crack detection and subpixel accuracy crack width determination as a tool for civil engineering material testing, the paper shows an algorithmic approach to handle widening cracks with relative rotations between related crack borders. In the first time step under zero-load, a set of points to be tracked through consecutive frames of an image sequence is defined. Then, subpixel-precise displacement fields are computed for the image data of the following time steps using an 8-parameter least-squares matching approach. The points are triangulated into a mesh, and the changes of the inner geometry of the triangles are considered with a mathematical model assuming a split of each triangle. With this model, subpixel-precise deformation vectors are derived. Crack candidates are determined by a thresholding applied to the vectors’ lengths. After an estimation of the crack normal, a decomposition of the deformation vectors is applied, allowing to compute crack widths and shear movements. As a novel contribution to the technique, a model extension is proposed for the case of a relative rotation between the crack borders in order to reduce systematic errors. The model includes two separate rigid transformations for each crack side.

3D concrete printing (3DCP) promises progress in the automation of the construction industry. The complexity and high quality requirements of 3DCP require automatic and digital control processes and systems that can continuously assess quality at any time and any place. In this paper, the relationship between the surface moisture of concrete and the degree of linear polarization (DolP) of light reflected from the concrete surface is investigated, which could serve as a basis for a camera-based control system. To proof this correlation, extensive practical investigations were carried out in which a mold-cast concrete specimen was illuminated with a white LED spotlight. The reflected light was recorded with a polarization camera, and the weight of the specimen served as a reference for the surface moisture. 14 experimental tests showed a high correlation between mass loss and DolP with a mean correlation coefficient of 0.994 in the last 60 minutes of the 180-minute observation period. The time series show an increasing DolP at the beginning of the observation period, which is probably due to the concrete bleeding and does not correlate with the mass loss. Further investigation into the factors influencing the DolP in the first part of the observation period needs to be carried out, either by testing influencing factors such as concrete bleeding or by finding a reliable method to measure surface moisture to correlate it with the DolP over the entire observation period. Nevertheless, the concept and methodology of data processing has been developed and prototyped, and a correlation has been demonstrated, that can provide a basis for the development of an operational camera-based concrete moisture monitoring system.

Accurate digital representation of indoor facilities is a key component for the generation of building twins. 3D indoor scenes are often reconstructed from 3D point clouds obtained by various measurement techniques, which usually show different accuracy characteristics. During the reconstruction process, the uncertainties of data and intermediate products propagate into the accuracy of the vectorized model. Although point clouds-based 3D building modeling has been a hot topic of research for at least two decades, a thorough analysis of error propagation for this problem from a geodetic point of view is still underrepresented. In this contribution, we propose an analytical approach to estimate the uncertainty of 3D modeling results using the analytic approach based on first-order Taylor-series expansion. A general model for the input data is established and the uncertainty expressions of all computed products are symbolically derived. We estimate the uncertainty of 3D data fitting, followed by the derivation of vectorized building parameters and their covariance matrices. The results of the theoretical approaches are tested on real data presenting an indoor scene. The practical example is illustrated, thoroughly analysed, and quantified.

Accurate information on turbidity in water bodies is relevant to numerous limnological and oceanological issues. However, the collection of turbidity parameters using conventional in-situ measurement methods is time-consuming and cost-intensive and therefore usually limited to very small study areas. The use of airborne LiDAR bathymetry data is a promising alternative. However, existing methods for deriving turbidity parameters from airborne LiDAR bathymetry data are limited to the determination of one single turbidity parameter per water column element. The paper presents a novel approach that overcomes the existing limitations enables the determination of 3D water turbidity fields. By volumetric data analysis, the vertical turbidity stratification in the water body can be determined. For validation purposes, the approach was applied to synthetic measurement data generated in a simulation as well as a real measurement data set of a shallow coastal water.

3D building modeling is a diverse field of research with a multitude of challenges, where data integration is an inherent component. The intensively growing market of BIM-related consumer applications requires methods and algorithms that enable efficient updates of existing 3D models without the need for cost-intensive data capturing and repetitive reconstruction processes. We propose a novel approach for semantic enrichment of existing indoor models by window objects, based on amateur camera RGB images with unknown exterior orientation parameters. The core idea of the approach is the parallel estimation of image camera poses with semantic recognition of target objects and their automatic mapping onto a 3D vector model. The presented solution goes beyond pure texture matching and links deep learning detection techniques with camera pose estimation and 3D reconstruction. To evaluate the performance of our procedure, we compare the estimated camera parameters with reference data, obtaining median values of 13.8 cm for the camera position and 1.1° for its orientation. Furthermore, a quality of 3D mapping is assessed based on the comparison to the reference 3D point cloud. All the windows presented in the data source were detected successfully, with a mean distance between both point sets equal to 3.6 cm. The experimental results prove that the presented approach achieves accurate integration of objects extracted from single images with an input 3D model, allowing for an effective increase of its semantic coverage.

The paper introduces 3D least squares matching as a technique to analyze multi-temporal micro-tomography data in civil engineering material testing. Time series of tomography voxel data sets are recorded during an in-situ tension test of a strain-hardening cement-based composite probe at consecutive load steps. 3D least squares matching is a technique to track cuboids in consecutive voxel data sets minimizing the sum of the squares of voxel value differences after a 12-parameter 3D affine transformation. For a regular grid of locations in each voxel data set of the deformed states, a subvoxel-precise 3D displacement vector field is computed. Discontinuities in these displacement vector fields indicate the occurrence of cracks in the probes during the load tests. These cracks are detected and quantitatively described by the computation of principal strains of tetrahedrons in a tetrahedral mesh, that is generated between the matching points. The subvoxel-accuracy potential of the technique allows the detection of very small cracks with a width much smaller than the actual voxel size.

Airborne LiDAR bathymetry is an efficient technique for surveying the bottom of shallow waters. In addition, the measurement data contain valuable information about the local turbidity conditions in the water body. The extraction of this information requires appropriate evaluation methods examining the decay of the recorded waveform signal. Existing approaches are based on several assumptions concerning the influence of the ALB system on the waveform signal, the extraction of the volume backscatter, and the directional independence of turbidity. The paper presents a novel approach that overcomes the existing limitations using two alternative turbidity estimation methods as well as different variants of further processed full-waveform data. For validation purposes, the approach was applied to a data set of a shallow inland water. The results of the quantitative evaluation show, which method and which data basis is best suited for the derivation of area wide water turbidity information.

This paper will describe a novel approach for the calibration of an underwater laser triangulation system. Underwater triangulation systems, consisting of a line laser and a camera can be used to determine the geometry of submerged objects or the topography of a water body bottom. Placing camera and laser line projector inside a waterproof housing leads to refraction effects at the air-glass-water interfaces, both of the laser light-sheet and image rays. This implies a deformed laser plane in the water and a curved line on the object surface. The proposed approach strictly models the geometry between camera, laser and housing. First experiments show, that the calibration method can be applied for water depth measurements with accuracies of 0.2–0.3 mm at depths in the order of 100 mm.

A critical part of Automated Material Handling Systems (AMHS) is the task allocation and dispatching strategy employed. In order to better understand and investigate this component, we here present an extensive experimental evaluation of three different approaches with randomly generated, as well as custom designed, environment configurations. While previous studies typically focused on use cases based on highly constrained navigation capabilities (e.g., overhead hoist transport systems), our evaluation is built around highly mobile, free-ranging vehicles, i.e., Autonomous Mobile Robots (AMR) that are gaining popularity in a broad range of applications. Consequently, our experiments are conducted using a microscopic agent-based simulation, instead of the more common discrete-event simulation model. Dispatching methods often are built around the assumption of the asynchronous evaluation of an event-based model, i.e., vehicles trigger a cascade of individual dispatching decisions, e.g., when reaching intersections. We find that this does not translate very well to a fleet of highly mobile systems that can change direction at any time. With this in mind, we present formulations of well known dispatching approaches that are better suited for a synchronous evaluation of the dispatching decisions. The formulations are based on the Stable Marriage Problem (SMP) and the Linear Sum Assignment Problem (LSAP). We use matching and assignment algorithms to compute the actual dispatching decisions. The selected algorithms are evaluated in a multi-agent simulation environment. To integrate a centralised fleet management, a digital twin concept is proposed and implemented. By this approach, the fleet management is independent of the implementation of the specific agents, allowing to quickly adapt to other simulation-based or real application scenarios. For the experimental evaluation, two new performance measures related to the efficiency of a material handling system are proposed, Travel Efficiency and Throughput Effort. The experimental evaluation indicates that reassignment mechanisms in the dispatching method can help to increase the overall efficiency of the fleet. We did not find significant differences in absolute performance in terms of throughput rate. Additionally, the difference in performance between SMP- and LSAP-based dispatching with reassignment seems negligible. We conclude with a discussion, where we consider potential confounding factors and relate the findings to previously reported results found in the literature.

Pixel-wise classification of remote sensing imagery is highly interesting for tasks like land cover classification or change detection. The acquisition of large training data sets for these tasks is challenging, but necessary to obtain good results with deep learning algorithms such as convolutional neural networks (CNN). In this paper we present a method for the automatic generation of a large amount of training data by combining satellite imagery with reference data from an available geospatial database. Due to this combination of different data sources the resulting training data contain a certain amount of incorrect labels. We evaluate the influence of this so called label noise regarding the time difference between acquisition of the two data sources, the amount of training data and the class structure. We combine Sentinel-2 images with reference data from a geospatial database provided by the German Land Survey Office of Lower Saxony (LGLN). With different training sets we train a fully convolutional neural network (FCN) and classify four land cover classes (Building, Agriculture, Forest, Water). Our results show that the errors in the training samples do not have a large influence on the resulting classifiers. This is probably due to the fact that the noise is randomly distributed and thus, neighbours of incorrect samples are predominantly correct. As expected, a larger amount of training data improves the results, especially for the less well represented classes. Other influences are different illuminations conditions and seasonal effects during data acquisition. To better adapt the classifier to these different conditions they should also be included in the training data.

Cadastral mapping of favela’s agglomerated buildings in informal settlements at Level of Detail 1 (LoD1) usually requires specific surveys and extensive manual data processing. Therefore, there is a demand for including the favelas in the city map production on the basis of Lidar surveys, as well as the detection of their vertical growth. However, the currently developed algorithms for automatically extracting buildings from airborne Lidar data have mainly been tested only for regular building reconstruction. This study aims to develop a Lidar data processing pipeline enabling to compute metrics related to intraurban informal settlements. To do so, we present a procedure to generate favela’s buildings delineation, height, floors’ number and built area and apply them to six case studies in favela typo-morphologies. We conducted an exploratory analysis in order to obtain the adequate parameters of the processing pipeline and its evaluation, using open source, free license and self-developed software. The results are compared to reference data from the manual stereo plotting, achieving a quality index in the building reconstruction about 70%. We also calculated the growth density, measured by gross Floor Area Ratio index inside settlement, revealing values from 29% to 74% considering different time periods.

E-learning is increasingly used to support student learning in higher education, facilitating administration of online formative assessments. Although providing diagnostic, actionable feedback is generally more effective, in current practice, feedback is often given in the form of a simple proportion of correctly solved items. This study shows the validation process of constructing detailed diagnostic information on a set of skills, abilities, and cognitive processes (so-called attributes) from students’ item response data with diagnostic classification models. Attribute measurement in the domain of statistics education is validated based on both expert judgment and empirical student data from a think-aloud study and large-scale assessment administration. The constructed assessments provide a valid and reliable measurement of the attributes. Inferences that can be drawn from the results of these formative assessments are discussed and it is demonstrated how this information can be communicated to students via learning dashboards to allow them to make more effective learning choices.

Single-crystal sapphire is utilized as a high-performance engineering material, especially in extreme and harsh environments. However, due to its extreme hardness and brittleness, the machinability of sapphire is still a challenge. By means of nanoindentation and plunge-cut experiments, the anisotropic brittle-ductile transition of the prismatic M-plane and rhombohedral R-plane is examined by analyzing crack morphologies and the critical depth-of-cut (CDC). The experimental results of the nanoindentation tests are correlated to the plunge-cut experiment. Both the prism plane and the rhombohedral crystal plane exhibit a two-fold symmetry of ductility with various crack patterns along the machined grooves. The direction-dependent plasticity of the hexagonal sapphire crystal is mainly connected to a twinning process accompanied by slip dislocation. Keywords: Sapphire, Anisotropy, Brittle-ductile transition, Orthogonal cutting

Grassland ecology experiments in remote locations requiring quantitative analysis of the biomass in defined plots are becoming increasingly widespread, but are still limited by manual sampling methodologies. To provide a cost-effective automated solution for biomass determination, several photogrammetric techniques are examined to generate 3D point cloud representations of plots as a basis, to estimate aboveground biomass on grassland plots, which is a key ecosystem variable used in many experiments. Methods investigated include Structure from Motion (SfM) techniques for camera pose estimation with posterior dense matching as well as the usage of a Time of Flight (TOF) 3D camera, a laser light sheet triangulation system and a coded light projection system. In this context, plants of small scales (herbage) and medium scales are observed. In the first pilot study presented here, the best results are obtained by applying dense matching after SfM, ideal for integration into distributed experiment networks.

A correlation between patellar kinematics and anterior knee pain is widely accepted. However, there is no consensus on how they are connected or what profile of patellar kinematics would minimize anterior knee pain. Nevertheless, answering this question by merging existing studies is further complicated by the variety of ways to describe patellar kinematics. Therefore, this study describes the most frequently used conventions for defining patellar kinematics, focusing on the rotations. The similarities and differences between the Cardan sequences and angles calculated by projecting axes are analyzed. Additionally, a tool is provided to enable the conversion of kinematic data between definitions in different studies. The choice of convention has a considerable impact on the absolute values and the clinical characteristics of the patello-femoral angles. In fact, the angles that result from using different mathematical conventions to describe a given patello-femoral rotation from our analyses differ up to a Root Mean Squared Error of 111.49° for patellar flexion, 55.72° for patellar spin and 35.39° for patellar tilt. To compare clinical kinematic patello-femoral results, every dataset must follow the same convention. Furthermore, researchers should be aware of the used convention’s implications to ensure reproducibility when interpreting and comparing such data.

Although simulation models are heavily used in biomechanical research and testing of TKA implants, pre-clinical tools for a holistic estimation of implant performance under dynamic loading conditions are rare. The objective of this study was the development of an efficient pre-clinical test method for analyzing knee contact mechanics and kinematics based on a dynamic FE model and to evaluate the effects of the proximo-distal patella position and the patellar tendon stiffness on the patellar kinematics. A finite element-based workflow for knee prostheses designs was developed based on standardized in vivo load data, which included the tibial forces and moments. In a new research approach, the tibial forces are used as input for the model, whereas the tibial moments were used to validate the results. For the standardized sit down, stand up, and knee bend load cycles, the calculated tibial moments show only small deviations from the reference values—especially for high flexion angles. For the knee bend cycle, the maximum absolute value of patellar flexion decreases for higher patellar tendon stiffness and more distally placed patellar components. Therefore, patella-related clinical problems caused by patella baja may also arise if the patellar tendon is too weak for high tibiofemoral flexion angles.

The paper at hand presents an investigation of the tensile behavior of high-strength, strain-hardening cement-based composites (HS-SHCC), reinforced with a single layer of continuous, two-dimensional textile made of ultra-high molecular weight polyethylene (UHMWPE). Uniaxial tension tests were performed on the bare UHMWPE textiles, on plain HS-SHCC, and on the hybrid fiber-reinforced composites. The bond properties between the textile yarns and the surrounding composite were investigated in single-yarn pullout experiments. In order to assess the influence of bond strength between the yarn and HS-SHCC on the tensile behavior of the composites with hybrid fiber reinforcement, the textile samples were analyzed both with, and without, an additional coating of epoxy resin and sand. Compared to the composites reinforced with carbon yarns in previous studies by the authors, the high elongation capacity of the UHMWPE textile established the higher strain capacity of the hybrid fiber-reinforced composites, and showed superior energy absorption capacity up to failure. The UHMWPE textile limited the average crack width in comparison with that of plain HS-SHCC, but led to slightly larger crack widths when compared to equivalent composites reinforced with carbon textile, the reason for which was traced back to the lower Young’s modulus and the higher elongation capacity of the polymer textile.

Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.

In recent years, many glaciers all over the world have been distinctly retreating and thinning. One of the consequences of this is the increase of so called glacier lake outburst flood events (GLOFs). The mechanisms ruling such GLOF events are still not yet fully understood by glaciologists. Thus, there is a demand for data and measurements that can help to understand and model the phenomena. Thereby, a main issue is to obtain information about the location and formation of subglacial channels through which some lakes, dammed by a glacier, start to drain. The paper will show how photogrammetric image sequence analysis can be used to collect such data. For the purpose of detecting a subglacial tunnel, a camera has been installed in a pilot study to observe the area of the Colonia Glacier (Northern Patagonian Ice Field) where it dams the Lake Cachet II. To verify the hypothesis, that the course of the subglacial tunnel is indicated by irregular surface motion patterns during its collapse, the camera acquired image sequences of the glacier surface during several GLOF events. Applying tracking techniques to these image sequences, surface feature motion trajectories could be obtained for a dense raster of glacier points. Since only a single camera has been used for image sequence acquisition, depth information is required to scale the trajectories. Thus, for scaling and georeferencing of the measurements a GPS-supported photogrammetric network has been measured. The obtained motion fields of the Colonia Glacier deliver information about the glacier’s behaviour before during and after a GLOF event. If the daily vertical glacier motion of the glacier is integrated over a period of several days and projected into a satellite image, the location and shape of the drainage channel underneath the glacier becomes visible. The high temporal resolution of the motion fields may also allows for an analysis of the tunnels dynamic in comparison to the changing water level of the lake.

The determination of crack propagation velocities can provide valuable information for a better understanding of damage processes of concrete. The spatio-temporal analysis of crack patterns developing at a speed of several hundred meters per second is a rather challenging task. In the paper, a photogrammetric procedure for the determination of crack propagation velocities in concrete specimens using high-speed camera image sequences is presented. A cascaded image sequence processing which starts with the computation of displacement vector fields for a dense pattern of points on the specimen’s surface between consecutive time steps of the image sequence chain has been developed. These surface points are triangulated into a mesh, and as representations of cracks, discontinuities in the displacement vector fields are found by a deformation analysis applied to all triangles of the mesh. Connected components of the deformed triangles are computed using region-growing techniques. Then, the crack tips are determined using the principal component analysis. The tips are tracked in the image sequence and the velocities between the time stamps of the images are derived. A major advantage of this method as compared to the established techniques is in the fact that it allows spatio-temporally resolved, full-field measurements rather than point-wise measurements. Furthermore, information on the crack width can be obtained simultaneously. To validate the experimentation, the authors processed image sequences of tests on four compact-tension specimens performed on a split-Hopkinson tension bar. The images were taken by a high-speed camera at a frame rate of 160,000 images per second. By applying the developed image sequence processing procedure to these datasets, crack propagation velocities of about 800 m/s were determined with a precision in the order of 50 m/s.

The Project ADFEX (Adaptive Federative 3D Exploration of Multi Robot System) pursues the goal to develop a time- and cost-efficient system for exploration and monitoring task of unknown areas or buildings. A fleet of unmanned aerial vehicles equipped with appropriate sensors (laser scanner, RGB camera, near infrared camera, thermal camera) were designed and built. A typical operational scenario may include the exploration of the object or area of investigation by an UAV equipped with a laser scanning range finder to generate a rough point cloud in real time to provide an overview of the object on a ground station as well as an obstacle map. The data about the object enables the path planning for the robot fleet. Subsequently, the object will be captured by a RGB camera mounted on the second flying robot for the generation of a dense and accurate 3D point cloud by using of structure from motion techniques. In addition, the detailed image data serves as basis for a visual damage detection on the investigated building. This paper focuses on our experience with use of a low-cost light-weight Hokuyo laser scanner onboard an UAV. The hardware components for laser scanner based 3D point cloud acquisition are discussed, problems are demonstrated and analyzed, and a quantitative analysis of the accuracy potential is shown as well as in comparison with structure from motion-tools presented.

Glacier Lake Outburst Floods (GLOFs) depict an environmental risk with an increasing damage potential in many regions of the world. GLOFs are often caused by glacier margin lakes, which suddenly find a drainage path underneath the bottom of a glacier, which is destabilized and retreating as a consequence of local or global climate changes. In a typical GLOF event, a glacier margin lake may drain completely in 24 hours, causing a large flood wave in the area downstream the glacier. The paper documents some recent GLOF events in the Northern Patagonian Icefield (Chile) and presents a terrestrial photogrammetric glacier margin lake monitoring system. The system is based on a camera taking images at regular time intervals. In these images, variations of the water level can be detected by tracking the water-land interface at pre-defined image spots. Due to the drainage mechanism, which is characterized by progressive erosion and melting at the bottom of the glacier, GLOFs are indicated by a progressive water level drop in the lake. Water level changes may be detected with subpixel accuracy by image sequence processing methods. If a 3D model of the lake bottom topography (or at least one height profile through the lake) exists, water level changes in monoscopic image sequences may be transformed into volume loss. The basic idea herein is the intersection of a terrain profile with a water level detected in the image and projected into object space. The camera orientation is determined through a GPS-supported photogrammetric network. Camera orientation changes, which may for instance be induced by wind, can be compensated by tracking some fiducial marks in the image. The system has been used in a pilot study at two glacier margin lakes in the Northern Patagonian Icefield. These lakes have a depth of about 80 - 100 meters. The larger one has a length of 5 km and a maximum volume of about 200,000,000 cubic meters. During the pilot study, several GLOF events could be recorded and processed. Water level changes can be determined at decimeter level precision. The results prove the feasibility of the concept, which has to be completed by a data telemetry and alarm system.

Terrestrial laser scanners generate dense and accurate 3D point clouds with minimal effort, which represent the geometry of real objects, while image data contains texture information of object surfaces. Based on the complementary characteristics of both data sets, a combination is very appealing for many applications, including forest-related tasks. In the scope of our research project, independent data sets of a plain birch stand have been taken by a full-spherical laser scanner and a hemispherical digital camera. Previously, both kinds of data sets have been considered separately: Individual trees were successfully extracted from large 3D point clouds, and so-called forest inventory parameters could be determined. Additionally, a simplified tree topology representation was retrieved. From hemispherical images, leaf area index (LAI) values, as a very relevant parameter for describing a stand, have been computed. The objective of our approach is to merge a 3D point cloud with image data in a way that RGB values are assigned to each 3D point. So far, segmentation and classification of TLS point clouds in forestry applications was mainly based on geometrical aspects of the data set. However, a 3D point cloud with colour information provides valuable cues exceeding simple statistical evaluation of geometrical object features and thus may facilitate the analysis of the scan data significantly.

Structural Health Monitoring (SHM) intends to identify damage by changes of characteristics as for instance the modal parameters. The eigenfrequencies, mode-shapes and damping-values are either directly used as damage indicators or the changes of derived parameters are analysed, such as e.g. flexibilities or updated finite element models. One common way is a continuous monitoring under environmental excitation forces, such as wind or traffic, i.e. the so-called output-only modal analysis. Alternatively, a forced measured external excitation in distinct time-intervals may be used for input-output modal analysis. Both methods are limited by the precision or the repeatability under real-life conditions at site. The paper will summarize several field tests of artificially step-by-step damaged bridges prior to their final demolishment and it will show the changes of eigenfrequencies due to induced artificial damage. Additionally, some results of a monitoring campaign of a healthy bridge in Luxembourg are presented. Reinforced concrete shows non-linear behaviour in the sense that modal parameters depend on the excitation force amplitude, i.e. higher forces lead often to lower eigenfrequencies than smaller forces. Furthermore, the temperature of real bridges is neither constant in space nor in time, while for instance the stiffness of asphalt is strongly dependant on it. Finally, ageing as such can also change a bridge’s stiffness and its modal parameters, e.g. because creep and shrinkage of concrete or ageing of elastomeric bearing pads influence their modulus of elasticity. These effects cannot be considered as damage, though they influence the measurement of modal parameters and hinder damage detection.

In the time of increasing maintenance costs, the continuous inspection and the earliest possible damage detection become more and more important. In order to minimize future maintenance costs, the exact evaluation of the condition of the structure and the exact assessment of potential damages are of essential importance. Therefore the University of Luxembourg carries out projects to investigate an efficient application of different assessment methods taking into account praxis relevant test conditions. As a part of this project especially the changing temperatures which influence the stiffness of the materials are analysed. As a consequence, for the condition assessment of structures, the asphalt layer cannot only be taken into consideration as a mass applied as load on the structure. Due to bond effects of the asphalt layer to the load carrying element its changing stiffness induced by changing temperatures influences the stiffness of the whole structure. Within this paper this effect will be illustrated. First the load carrying behaviour and the stiffness of pre-stressed concrete slabs realized with and without an additional asphalt layer will be investigated in a climate chamber and the results will be compared for different temperatures.

The paper describes and compares the workflows and results of generating digital elevation models (DEMs) of underwater areas from airborne laser scanning and aerial stereo images. Based on a combined laser scanning/image data set of an artificial lake, both methods are described and pros/cons are highlighted. The authors focus on the final results, especially on accuracy, completeness and spatial resolution of the underwater DEM’s. Further, practical aspects of processing and complexity of both methods are highlighted too.

Thanks to the rapid technological progress in the field of mobile devices, smartphones are increasingly becoming valuable for science. They can serve as photogrammetric measurement devices with built-in cameras, micro-electro-mechanical systems for orientation and position assessment, as well as powerful processing units allowing field-based data acquisition and processing. This paper outlines a comprehensive investigation focusing on the accuracy and stability of smartphone camera rotation parameters determined by built-in smartphone sensors. For that purpose, the rotation parameters were measured under a range of different conditions. Four test scenarios were defined considering indoor- and outdoor measurements using three different devices being in static and dynamic modes. Furthermore, the influence of magnetic perturbations was investigated. The rotation parameters were determined from the measurements applying different sensor fusion approaches. Reference values for accuracy assessment were provided by a superior precision inertial measurement unit that measured the rotation parameters simultaneously to the smartphone in each experiment. The analysis of the smartphone-based rotation parameters, separated in the Euler angles azimuth, pitch and roll, shows average accuracies below 2° for pitch and roll. In comparison, azimuth shows significantly lower accuracies of more than 30° especially when the smartphone is in motion and when it is exposed to magnetic perturbations. In this regard, advanced multi-sensor fusion approaches were examined that handle such interferences to considerably improve the accuracy of azimuth measurements. In conclusion, a summary of accuracies and stabilities to be expected from smartphone sensors is given referring to ambient conditions and investigated sensor fusion strategies.

A network consisting of six cameras was set up on both sides of Lange Glacier on King George Island, Antarctica, for a period of two years to monitor changes in the glacier’s motion behaviour. The cameras were observing spatio-temporal glacier surface velocity vector fields as well as the position of the glacier front. Velocity field information was obtained from image sequences by applying subpixel accuracy photogrammetric image sequence analysis techniques. Georeferencing in a superordinate coordinate system was performed via integrated photogrammetric-geodetic network adjustment. As a result, velocity fields were determined with maximum glacier surface velocities in the order of 1.5 meter per day. The results of terrestrial camera image sequence processing can be used as validation and calibration reference for satellite image based glacier velocity dynamics calculations. Moreover, the very high temporal resolution of the image sequences taken at 20 minute time intervals can also be used to analyse highly dynamic processes.

We present calving flux, calving rate, frontal retreat and volume change rate results in Lange Glacier from 2015 to 2017, including also velocity calculations in 2015, 2017 and 2018. The peak velocities at glacier front were between 1.0 and 1.5 [md-1] near the center line with a maximum frontal retreat of 100 [m]. The glacier presents almost no volume change rate during the period analyzed and the average ice discharge is 0.026 [km3 yr-1].

This publication concentrates on the photogrammetric crack width measurement of crack patterns of concrete probes under impact loading in high-speed stereo image sequences. The presented algorithm works for non-planar specimens with deformations that only appear tangential to the surface and the method is based on triangle mesh analysis. Experiments were conducted with cylindrical specimens with an impact load affecting parallel to the main axis of the cylinder.

For the correct usage and analysis within a BIM environment, image-based point clouds that were created with Structure from Motion (SfM) tools have to be transformed into the building coordinate system via a seven parameter Helmert Transformation. Usually control points are used for the estimation of the transformation parameters. In this paper we present a novel, highly automated approach to calculate these transformation parameters without the use of control points. The process relies on the relationship between wall respectively plane information of the BIM and three-dimensional line data that is extracted from the image data. In a first step, 3D lines are extracted from the oriented input images using the tool Line3D++. These lines are defined by the 3D coordinates of the start and end points. Afterwards the lines are matched to the planes originating from the BIM model representing the walls, floors and ceilings. Besides finding a suitable functional and stochastic model for the observation equations and the adjustment calculation, the most critical aspect is finding a correct match for the lines and the planes. We therefore developed a RANSAC-inspired matching algorithm to get a correct assignment between elements of the two data sources. Synthetic test data sets have been created for evaluating the methodology.

Airborne LiDAR bathymetry allows an efficient and area-wide acquisition of water bottom points in shallow water areas. However, the measurement method is severely limited by water turbidity, impending a reliable detection of water bottom points at higher turbidity or in deeper water bodies. This leads to an incomplete acquisition of the water bottom topography. In this contribution, advanced processing methods are presented, which increase the penetration depth compared to the original processed data and enable a reliable extraction and detection of bottom points in deeper water bodies. The methodology is based on the analysis of correlated neighborhood information assuming a steady water bottom. The results confirm a significantly higher penetration depth with a high reliability of the additionally extracted water bottom points along with a larger coverage of the water bottom topography.

The automatic extraction of windows from photogrammetric data has achieved increasing attention in recent times. An unsupervised windows extraction approach from photogrammetric point clouds with thermal attributes is proposed in this study. First, point cloud segmentation is conducted by a popular workflow: Multiscale supervoxel generation is applied to the image-based 3D point cloud, followed by region growing and energy optimization using spatial positions and thermal attributes of the raw points. Afterwards, an object-based feature (window index) is extracted using the average thermal attribute and the size of the object. Next, thresholding is applied to extract initial window regions. Finally, several criterions are applied to further refine the extraction results. For practical validation, the approach is evaluated on an art nouveau building row façade located at Dresden, Germany.

Airborne Lidar Bathymetry is a laser scanning technique to measure waterbody bottom topography in shallow waterbodies with limited turbidity. The topic has recently gained relevance due to the advent of new sensor technologies allowing for much higher spatial resolution in bathymetry data capture and due to guidelines demanding regular monitoring of waterbodies. In our contribution, we focus on three important aspects of lidar bathymetry: In the first part, systematic effects of wave patterns will be analysed in order to derive waterbody coordinate correction terms. In the second part, we will apply waveform-stacking techniques to enhance the detectability of water bottom points in lidar bathymetry full waveform signals. In the third part, a dedicated full wave-form analysis procedure is shown, which allows for deriving turbidity information from the decay of the signal intensity in the waterbody.

Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data.

The paper presents an approach for the generation of digital elevation models (DEMs) of underwater areas from aerial images. Standard software-products do not provide the possibility to measure correctly through refractive interfaces, such as water. Existing solutions for that problem are based on oriented images and known water levels with the DEM points determined by forward intersection based on reconstructed image ray paths (ray tracing). In this article we present an integrated procedure for image orientation as well as DEM mass point determination from aerial imagery containing both land and underwater areas. The proof of concept was done by capturing UAV imagery of shallow water areas of a high-alpine lake in the Swiss alps. In the paper the processed dataset will be presented. Furthermore, the extraction and matching of image-points observed through water are discussed. The accuracy potential as well as practical limitations of processing multimedia-data are analysed.

Rivers with small- and medium-scaled catchments have been increasingly affected by extreme events, i.e. flash floods, in the last years. New methods to describe and predict these events are developed in the interdisciplinary research project EXTRUSO. Flash flood events happen on small temporal and spatial scales, stressing the necessity of high-resolution input data for hydrological and hydrodynamic modelling. Among others, the benefit of high-resolution digital terrain models (DTMs) will be evaluated in the project. This article introduces a boat-based approach for the acquisition of geometrical and morphological data of small rivers and their banks. An unmanned water vehicle (UWV) is used as a multi-sensor platform to collect 3D-point clouds of the riverbanks, as well as bathymetric measurements of water depth and river morphology. The UWV is equipped with a mobile Lidar, a panorama camera, an echo sounder and a positioning unit. Whole (sub-) catchments of small rivers can be digitalized and provided for hydrological modelling when UWV-based and UAV (unmanned aerial vehicle) based point clouds are fused.

Airborne LiDAR bathymetry (ALB) requires a refraction correction on the basis of Snell’s law at the air-water interface and a speedof- light correction to be applied on the raw laser data in order to achieve a geometric accurate representation of the water bottom. Strictly speaking, this requires exact knowledge about the local water surface inclination. If this information is not available, certain simplifications have to be introduced in correction methods. Common correction methods assume either a horizontal or a locally tilted planar water surface as well as an infinitesimally small thin laser ray, thus neglecting effects caused by the finite laser pulse diameter penetrating a curved surface. In our simulation approach, the refraction of finite diameter laser pulses passing the air/water interface is modeled differentially in a strict manner. The simulation tool is able to predict wave induced coordinate errors which have to be expected due to the neglections made in common refraction correction methods. Moreover, wave pattern dependent correction terms were be derived from systematic portions of the errors revealed by the simulations. The goal of this paper is to experimentally validate the coordinate errors predicted by the simulation tool. For that purpose, airborne laser bathymetry data of a 12 by 50 meter open air wave pool were processed, and the results were compared to reference data of the empty pool acquired by terrestrial laser scanning. The comparison showed that the effects predicted in the numerical simulation are confirmed by the experimental validation.

An automatic building façade thermal texture mapping approach, using uncooled thermal camera data, is proposed in this paper. First, a shutter-less radiometric thermal camera calibration method is implemented to remove the large offset deviations caused by changing ambient environment. Then, a 3D façade model is generated from a RGB image sequence using structure-from-motion (SfM) techniques. Subsequently, for each triangle in the 3D model, the optimal texture is selected by taking into consideration local image scale, object incident angle, image viewing angle as well as occlusions. Afterwards, the selected textures can be further corrected using thermal radiant characteristics. Finally, the Gauss filter outperforms the voted texture strategy at the seams smoothing and thus for instance helping to reduce the false alarm rate in façade thermal leakages detection. Our approach is evaluated on a building row façade located at Dresden, Germany.

An automatic spatio-temporal flow velocity measurement approach, using an uncooled thermal camera, is proposed in this paper. The basic principle of the method is to track visible thermal features at the water surface in thermal camera image sequences. Radiometric and geometric calibrations are firstly implemented to remove vignetting effects in thermal imagery and to get the interior orientation parameters of the camera. An object-based unsupervised classification approach is then applied to detect the interest regions for data referencing and thermal feature tracking. Subsequently, GCPs are extracted to orient the river image sequences and local hot points are identified as tracking features. Afterwards, accurate dense tracking outputs are obtained using pyramidal Lucas-Kanade method. To validate the accuracy potential of the method, measurements obtained from thermal feature tracking are compared with reference measurements taken by a propeller gauge. Results show a great potential of automatic flow velocity measurement in small rivers using imagery from a thermal camera.

This paper deals with the determination of crack widths of concrete beams during load tests from monocular image sequences. The procedure starts in a reference image of the probe with suitable surface texture under zero load, where a large number of points is defined by an interest operator. Then a triangulated irregular network is established to connect the points. Image sequences are recorded during load tests with the load increasing continuously or stepwise, or at intermittently changing load. The vertices of the triangles are tracked through the consecutive images of the sequence with sub-pixel accuracy by least squares matching. All triangles are then analyzed for changes by principal strain calculation. For each triangle showing significant strain, a crack width is computed by a thorough geometric analysis of the relative movement of the vertices.

Collisions between space debris and satellites in Earth’s orbits are not only catastrophic to the satellite, but also create thousands of new fragments, exacerbating the space debris problem. One challenge in understanding the space debris environment is the lack of data on fragmentation and breakup caused by hypervelocity impacts. In this paper, we present an experimental measurement technique capable of recording 3D position and velocity data of fragments produced by hypervelocity impact experiments in the lab. The experimental setup uses stereo high-speed cameras to record debris fragments generated by a hypervelocity impact. Fragments are identified and tracked by searching along trajectory lines and outliers are filtered in 4D space (3D + time) with RANSAC. The method is demonstrated on a hypervelocity impact experiment at 3.2 km/s and fragment velocities and positions are measured. The results demonstrate that the method is very robust in its ability to identify and track fragments from the low resolution and noisy images typical of high-speed recording.