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Open Access
Towards improved targetless registration and deformation analysis of TLS point clouds using patch-based segmentation
(2023) Yang, Yihui; Schwieger, Volker (Prof. Dr.-Ing. habil. Dr. h.c.)
The geometric changes in the real world can be captured by measuring and comparing the 3D coordinates of object surfaces. Traditional point-wise measurements with low spatial resolution may fail to detect inhomogeneous, anisotropic and unexpected deformations, and thus cannot reveal complex deformation processes. 3D point clouds generated from laser scanning or photogrammetric techniques have opened up opportunities for an area-wise acquisition of spatial information. In particular, terrestrial laser scanning (TLS) exhibits rapid development and wide application in areal geodetic monitoring owing to the high resolution and high quality of acquired point cloud data. However, several issues in the process chain of TLS-based deformation monitoring are still not solved satisfactorily. This thesis mainly focuses on the targetless registration and deformation analysis of TLS point clouds, aiming to develop novel data-driven methods to tackle the current challenges. For most deformation processes of natural scenes, in some local areas no shape deformations occur (i.e., these areas are rigid), and even the deformation directions show a certain level of consistency when these areas are small enough. Further point cloud processing, like stability and deformation analyses, could benefit from the assumptions of local rigidity and consistency of deformed point clouds. In this thesis, thereby, three typical types of locally rigid patches - small planar patches, geometric primitives, and quasi-rigid areas - can be generated from 3D point clouds by specific segmentation techniques. These patches, on the one hand, can preserve the boundaries between rigid and non-rigid areas and thus enable spatial separation with respect to surface stability. On the other hand, local geometric information and empirical stochastic models could be readily determined by the points in each patch. Based on these segmented rigid patches, targetless registration and deformation analysis of deformed TLS point clouds can be improved regarding accuracy and spatial resolution. Specifically, small planar patches like supervoxels are utilized to distinguish the stable and unstable areas in an iterative registration process, thus ensuring only relatively stable points are involved in estimating transformation parameters. The experimental results show that the proposed targetless registration method has significantly improved the registration accuracy. These small planar patches are also exploited to develop a novel variant of the multiscale model-to-model cloud comparison (M3C2) algorithm, which constructs prisms extending from planar patches instead of the cylinders in standard M3C2. This new method separates actual surface variations and measurement uncertainties, thus yielding lower-uncertainty and higher-resolution deformations. A coarse-to-fine segmentation framework is used to extract multiple geometric primitives from point clouds, and rigorous parameter estimations are performed individually to derive high-precision parametric deformations. Besides, a generalized local registration-based pipeline is proposed to derive dense displacement vectors based on segmented quasi-rigid areas that are corresponded by areal geometric feature descriptors. All proposed methods are successfully verified and evaluated by simulated and/or real point cloud data. The choice of proposed deformation analysis methods for specific scenarios or applications is also provided in this thesis.
Open Access
Confirmation of siderazot, Fe3N1.33, the only terrestrial nitride mineral
(2021) Bette, Sebastian; Theye, Thomas; Bernhardt, Heinz-Jürgen; Clark, William P.; Niewa, Rainer
Siderazot, the only terrestrial nitride mineral, was reported only once in 1876 to occur as coating on volcanic rocks in a fumarolic environment from Mt. Etna and, to date, has been neither confirmed nor structurally characterized. We have studied the holotype sample from the Natural History Museum, London, UK, originally collected by O. Silvestri in 1874, and present siderazot with epsilon-Fe3N-type crystal structure and composition of Fe3N1.33(7) according to crystal structure Rietveld refinements, in good agreement with electron microprobe analyses. Crystal structure data, chemical composition, and Raman and reflectance measurements are reported. Possible formation conditions are derived from composition and phase stability data according to synthetic samples.
Open Access
Analyzing and characterizing spaceborne observation of water storage variation : past, present, future
(2024) Saemian, Peyman; Sneeuw, Nico (Prof. Dr.-Ing.)
Water storage is an indispensable constituent of the intricate water cycle, as it governs the availability and distribution of this precious resource. Any alteration in the water storage can trigger a cascade of consequences, affecting not only our agricultural practices but also the well-being of various ecosystems and the occurrence of natural hazards. Therefore, it is essential to monitor and manage the water storage levels prudently to ensure a sustainable future for our planet. Despite significant advancements in ground-based measurements and modeling techniques, accurately measuring water storage variation remained a major challenge for a long time. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) satellites have revolutionized our understanding of the Earth's water cycle. By detecting variations in the Earth's gravity field caused by changes in water distribution, these satellites can precisely measure changes in total water storage (TWS) across the entire globe, providing a truly comprehensive view of the world's water resources. This information has proved invaluable for understanding how water resources are changing over time, and for developing strategies to manage these resources sustainably. However, GRACE and GRACE-FO are subject to various challenges that must be addressed in order to enhance the efficacy of our exploitation of GRACE observations for scientific and practical purposes. This thesis aims to address some of the challenges faced by GRACE and GRACE-FO. Since the inception of the GRACE mission, scholars have commonly extracted mass changes from observations by approximating the Earth's gravity field utilizing mathematical functions termed spherical harmonics. Various institutions have already processed GRACE(-FO) data, known as level-2 data in the GRACE community, considering the constraints, approaches, and models that have been utilized. However, this processed data necessitates post-processing to be used for several applications, such as hydrology and climate research. In this thesis, we evaluate various methods of processing GRACE(-FO) level-2 data and assess the spatio-temporal effect of the post-processing steps. Furthermore, we aim to compare the consistency between GRACE and its successor mission, GRACE-FO, in terms of data quality and measurement accuracy. By analyzing and comparing the data from these two missions, we can identify any potential discrepancies or differences and establish the level of confidence in the accuracy and reliability of the GRACE-FO measurements. Finally, we will compare the processed level-3 products with the level-3 products that are presently accessible online. The relatively short record of the GRACE measurements, compared to other satellite missions and observational records, can limit some studies that require long-term data. This short record makes it challenging to separate long-term signals from short-term variability and validate the data with ground-based measurements or other satellite missions. To address this limitation, this thesis expands the temporal coverage of GRACE(-FO) observations using global hydrological, atmospheric, and reanalysis models. First, we assess these models in estimating the TWS variation at a global scale. We compare the performance of various methods including data-driven and machine learning approaches in incorporating models and reconstruct GRACE TWS change. The results are also validated against Satellite Laser Ranging (SLR) observations over the pre-GRACE period. This thesis develops a hindcasted GRACE, which provides a better understanding of the changes in the Earth's water storage on a longer time scale. The GRACE satellite mission detects changes in the overall water storage in a specific region but cannot distinguish between the different compartments of TWS, such as surface water, groundwater, and soil moisture. Understanding these individual components is crucial for managing water resources and addressing the effects of droughts and floods. This study aims to integrate various data sources to improve our understanding of water storage variations at the continental to basin scale, including water fluxes, lake water level, and lake storage change data. Additionally, the study demonstrates the importance of combining GRACE(-FO) observations with other measurements, such as piezometric wells and rain-gauges, to understand the water scarcity predicament in Iran and other regions facing similar challenges. The GRACE satellite mission provides valuable insights into the Earth's system. However, the GRACE product has a level of uncertainty due to several error sources. While the mission has taken measures to minimize these uncertainties, researchers need to account for them when analyzing the data and communicate them when reporting findings. This thesis proposes a probabilistic approach to incorporate the Total Water Storage Anomaly (TWSA) data from GRACE(-FO). By accounting for the uncertainty in the TWSA data, this approach can provide a more comprehensive understanding of drought conditions, which is essential for decision makers managing water resources and responding to drought events.
Open Access
Forming a hybrid intelligence system by combining Active Learning and paid crowdsourcing for semantic 3D point cloud segmentation
(2023) Kölle, Michael; Sörgel, Uwe (Prof. Dr.-Ing.)
While in recent years tremendous advancements have been achieved in the development of supervised Machine Learning (ML) systems such as Convolutional Neural Networks (CNNs), still the most decisive factor for their performance is the quality of labeled training data from which the system is supposed to learn. This is why we advocate focusing more on methods to obtain such data, which we expect to be more sustainable than establishing ever new classifiers in the rapidly evolving ML field. In the geospatial domain, however, the generation process of training data for ML systems is still rather neglected in research, with typically experts ending up being occupied with such tedious labeling tasks. In our design of a system for the semantic interpretation of Airborne Laser Scanning (ALS) point clouds, we break with this convention and completely lift labeling obligations from experts. At the same time, human annotation is restricted to only those samples that actually justify manual inspection. This is accomplished by means of a hybrid intelligence system in which the machine, represented by an ML model, is actively and iteratively working together with the human component through Active Learning (AL), which acts as pointer to exactly such most decisive samples. Instead of having an expert label these samples, we propose to outsource this task to a large group of non-specialists, the crowd. But since it is rather unlikely that enough volunteers would participate in such crowdsourcing campaigns due to the tedious nature of labeling, we argue attracting workers by monetary incentives, i.e., we employ paid crowdsourcing. Relying on respective platforms, typically we have access to a vast pool of prospective workers, guaranteeing completion of jobs promptly. Thus, crowdworkers become human processing units that behave similarly to the electronic processing units of this hybrid intelligence system performing the tasks of the machine part. With respect to the latter, we do not only evaluate whether an AL-based pipeline works for the semantic segmentation of ALS point clouds, but also shed light on the question of why it works. As crucial components of our pipeline, we test and enhance different AL sampling strategies in conjunction with both a conventional feature-driven classifier as well as a data-driven CNN classification module. In this regard, we aim to select AL points in such a manner that samples are not only informative for the machine, but also feasible to be interpreted by non-experts. These theoretical formulations are verified by various experiments in which we replace the frequently assumed but highly unrealistic error-free oracle with simulated imperfect oracles we are always confronted with when working with humans. Furthermore, we find that the need for labeled data, which is already reduced through AL to a small fraction (typically ≪1 % of Passive Learning training points), can be even further minimized when we reuse information from a given source domain for the semantic enrichment of a specific target domain, i.e., we utilize AL as means for Domain Adaptation. As for the human component of our hybrid intelligence system, the special challenge we face is monetarily motivated workers with a wide variety of educational and cultural backgrounds as well as most different mindsets regarding the quality they are willing to deliver. Consequently, we are confronted with a great quality inhomogeneity in results received. Thus, when designing respective campaigns, special attention to quality control is required to be able to automatically reject submissions of low quality and to refine accepted contributions in the sense of the Wisdom of the Crowds principle. We further explore ways to support the crowd in labeling by experimenting with different data modalities (discretized point cloud vs. continuous textured 3D mesh surface), and also aim to shift the motivation from a purely extrinsic nature (i.e., payment) to a more intrinsic one, which we intend to trigger through gamification. Eventually, by casting these different concepts into the so-called CATEGORISE framework, we constitute the aspired hybrid intelligence system and employ it for the semantic enrichment of ALS point clouds of different characteristics, enabled through learning from the (paid) crowd.
Open Access
Porosity and permeability alterations in processes of biomineralization in porous media - microfluidic investigations and their interpretation
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Weinhardt, Felix; Class, Holger (apl. Prof. Dr.-Ing)
Motivation: Biomineralization refers to microbially induced processes resulting in mineral formations. In addition to complex biomineral structures frequently formed by marine organisms, like corals or mussels, microbial activities may also indirectly induce mineralization. A famous example is the formation of stromatolites, which result from biofilm activities that locally alter the chemical and physical properties of the environment in favor of carbonate precipitation. Recently, biomineralization gained attention as an engineering application. Especially with the background of global warming and the objective to reduce CO2 emissions, biomineralization offers an innovative and sustainable alternative to the usage of conventional Portland cement, whose production currently contributes significantly to global CO2 emissions. The most widely used method of biomineralization in engineering applications, is ureolytic calcium carbonate precipitation, which relies on the hydrolysis of urea and the subsequent precipitation of calcium carbonate. The hydrolysis of urea at moderate temperatures is relatively slow and therefore needs to be catalyzed by the enzyme urease to be practical for applications. Urease can be extracted from plants, for example from ground jack beans, and the process is consequently referred to as enzyme-induced calcium carbonate precipitation (ECIP). Another method is microbially induced calcium carbonate precipitation (MICP), which uses ureolytic bacteria that produce the enzyme in situ. EICP and MICP applications allow for producing various construction materials, stabilizing soils, or creating hydraulic barriers in the subsurface. The latter can be used, for example, to remediate leakages at the top layer of gas storage reservoirs, or to contain contaminant plumes in aquifers. Especially when remediating leakages in the subsurface, the most crucial parameter to be controlled is its intrinsic permeability. A valuable tool for predicting and planning field applications is the use of numerical simulation at the scale of representative elementary volumes (REV). For that, the considered domain is subdivided into several REV’s, which do not resolve the pore space in detail, but represent it by averaged parameters, such as the porosity and permeability. The porosity describes the ratio of the pore space to the considered bulk volume, and the permeability quantifies the ease of fluid flow through a porous medium. A change in porosity generally also affects permeability. Therefore, for REV-scale simulations, constitutive relationships are utilized to describe permeability as a function of porosity. There are several porosity-permeability relationships in the literature, such as the Kozeny-Carman relationship, Verma-Pruess, or simple power-law relationships. These constitutive relationships can describe individual states but usually do not include the underlying processes. Different boundary conditions during biomineralization may influence the course of porosity-permeability relationships. However, these relationships have not yet been adequately addressed. Pore-scale simulations are, in principle, very well suited to investigate pore space changes and their effects on permeability systematically. However, these simulations also rely on simplifications and assumptions. Therefore, it is essential to conduct experimental studies to investigate the complex processes during calcium carbonate precipitation in detail at the pore scale. Recent studies have shown that microfluidic methods are particularly suitable for this purpose. However, previous microfluidic studies have not explicitly addressed the impact of biomineralization on hydraulic effects. Therefore, this work aims to identify relevant phenomena at the pore scale to conclude on the REV-scale parameters, porosity and permeability, and their relationship. Contributions: This work comprises three publications. First, a suitable microfluidic setup and workflow were developed in Weinhardt et al. [2021a] to study pore space changes and the associated hydraulic effects reliably. This paper illustrated the benefits and insights of combining optical microscopy and micro X-ray computed tomography (micro XRCT) with hydraulic measurements in microfluidic chips. The elaborated workflow allowed for quantitative analysis of the evolution of calcium carbonate precipitates in terms of their size, shape, and spatial distribution. At the same time, their influence on differential pressure could be observed as a measure of flow resistance. Consequently, porosity and permeability changes could be determined. Along with this paper, we published two data sets [Weinhardt et al., 2021b, Vahid Dastjerdi et al., 2021] and set the basis for two other publications. In the second publication [von Wolff et al., 2021], the simulation results of a pore-scale numerical model, developed by Lars von Wolff, were compared to the experimental data of the first paper [Weinhardt et al., 2021b]. We observed a good agreement between the experimental data and the model results. The numerical studies complemented the experimental observations in allowing for accurate analysis of crystal growth as a function of local velocity profiles. In particular, we observed that crystal aggregates tend to grow toward the upstream side, where the supply of reaction products is higher than on the downstream side. Crystal growth during biomineralization under continuous inflow is thus strongly dependent on the locally varying velocities in a porous medium. In the third publication [Weinhardt et al., 2022a], we conducted further microfluidic experiments based on the experimental setup and workflow of the first contribution and published another data set [Weinhardt et al., 2022b]. We used microfluidic cells with a different, more realistic pore structure and investigated the influence of different injection strategies. We found that the development of preferential flow paths during EICP application may depend on the given boundary conditions. Constant inflow rates can lead to the development of preferential flow paths and keep them open. Gradually reduced inflow rates can mitigate this effect. In addition, we concluded that the coexistence of multiple calcium carbonate polymorphs and their transformations could influence the temporal evolution of porosity-permeability relationships.
Open Access
Use of non-linearity as a characteristic in the selection of filtering algorithms in kinematic positioning
(2020) Pham, Dung; Schwieger, Volker (Prof. Dr.-Ing. habil. Dr. h.c.)
Selection of an optimal filtering algorithm for kinematic positioning systems constitutes one of the most extensively studied applications in the surveyor engineering community. The ability of a filtering algorithm is often assessed through its performance. The performance of a filtering algorithm is frequently evaluated in terms of accuracy and computational time. According to the accuracy parameter, it is often determined by a comparison between true trajectory and the estimated one from an algorithm. However, the true trajectory is commonly unknown in real-life situations, and thus the accuracy of the filtering algorithm cannot be assessed in this manner. Indeed, lack of true trajectory is one of the primary obstacles in the evaluation of the performance of filtering algorithms. The non-linearity of the model, on the other hand, can be determined without any information about the true trajectory and is also associated with the abilities of algorithms. So far, however, very little attention has been paid to the role of the decision of filtering algorithms based on non-linearity. Thus, this study proposes an alternative characteristic in the assessment of the performance of filtering algorithms, which is the non-linearity of the observation model. This research aims to assess the ability of non-linear characteristic for the choice of an optimal filtering algorithm. In this research, the data are simulated by the Monte Carlo method. The abilities of filtering algorithms are investigated on the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). These algorithms are widely utilized in kinematic positioning, and they are appropriate for various levels of non-linearity. The current study evaluated the influence of the algorithm’s accuracy on three factors: measurement uncertainty, observation geometry, and the number of observations. These algorithms are also assessed on their computational times according to a certain scenario. Regarding measures of non-linearity, three different indicators are examined for the non-linearity of both system and observation models. The coefficient of determination, 1-R2, is utilized as a single indicator to measure the non-linearity of each function of the above models. The M and 1-MVA, known as the deviation of a non-linear function from linearity and multivariate association, respectively, can be used as indicators to quantify the non-linearity of numerous functions of the above models jointly. The 1-MVA indicator is proposed for the first time to quantify the non-linearity of models. From analyses of the accuracy and non-linearity, the relationship between them is determined with changing measurement uncertainty and observation geometry in several scenarios. Based on the established relationship between accuracy and non-linearity, the choice of an optimal algorithm is analyzed through numerical examples. These results indicate that the accuracy of these algorithms is strongly influenced by measurement uncertainty, observation geometry, and the number of observations. The accuracy obtained by PF is higher than that of UKF and EKF. Conversely, the computational time of EKF is shorter than that of UKF and PF. According to measures of non-linearity, the above-proposed indicators are suitable, and the tendency of non-linearity of a model obtained by these indicators is the same. The non-linearity of the system model is small due to the given small amount of standard deviations of the disturbance quantities. Inversely, the non-linearity of the observation model is high due to high measurement uncertainties, or poor observation geometries. The main finding of this research is that both non-linearity of the observation model and position accuracy are influenced by factors of measurement uncertainty and observation geometry. Therefore, the relationship between the position accuracy and the non-linearity of the observation model is established based on these factors. This relationship is strong, which is assessed by the goodness-of-fit value of the best fitting function. In addition, another important result from the present research is that the fitting function described for this relationship changes due to influencing factors of scenarios. The established relationship constitutes the main limitation of this characteristic in application. As a result, instead of accuracy, the non-linearity of the observation model can be employed for the assessment of algorithms when the true trajectory is not available. However, the optimal algorithm can only be selected using these factors in some special cases. For a general case of arbitrary scenarios’ factors, the non-linear characteristic cannot be used for this purpose.
Open Access
Using GRACE data to study the impact of snow and rainfall on terrestrial water storage in Northeast China
(2020) Qian, An; Yi, Shuang; Chang, Le; Sun, Guangtong; Liu, Xiaoyang
Water resources are important for agricultural, industrial, and urban development. In this paper, we analyzed the influence of rainfall and snowfall on variations in terrestrial water storage (TWS) in Northeast China from Gravity Recovery and Climate Experiment (GRACE) gravity satellite data, GlobSnow snow water equivalent product, and ERA5-land monthly total precipitation, snowfall, and snow depth data. This study revealed the main composition and variation characteristics of TWS in Northeast China. We found that GRACE provided an effective method for monitoring large areas of stable seasonal snow cover and variations in TWS in Northeast China at both seasonal and interannual scales. On the seasonal scale, although summer rainfall was 10 times greater than winter snowfall, the terrestrial water storage in Northeast China peaked in winter, and summer rainfall brought about only a sub-peak, 1 month later than the maximum rainfall. On the interannual scale, TWS in Northeast China was controlled by rainfall. The correlation analysis results revealed that the annual fluctuations of TWS and rainfall in Northeast China appear to be influenced by ENSO (EI Niño-Southern Oscillation) events with a lag of 2-3 years. In addition, this study proposed a reconstruction model for the interannual variation in TWS in Northeast China from 2003 to 2016 on the basis of the contemporary terrestrial water storage and rainfall data.
Open Access
Modellierung von Bodenerosion und Sedimentaustrag bei Hochwasserereignissen am Beispiel des Einzugsgsgebiets der Rems
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Schönau, Steffen; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)
Die vorliegende Dissertation untersucht Bodenerosion und Sedimentaustrag bei Hochwasserereignissen und Starkniederschlägen im Einzugsgebiet der Rems (Flussgebiet Neckar, Stromgebiet Rhein). Es werden die Grundlagen des Zusammenspiels von (Stark-) Niederschlag, Hochwasser und Sturzfluten, Bodenerosion und Sedimentaustrag sowie deren messtechnische und modellbasierte Erfassung dargestellt. Die Anwendung empirischer Modellansätze im Untersuchungsgebiet beinhaltet Modellparametrisierung, -kalibrierung und -validierung sowie Regionalisierung für die Übertragbarkeit auf unbeobachtete Gebiete. Es erfolgt eine Untersuchung des räumlichen Zusammenhangs der flächenhaften Eingangsdaten und Modellergebnisse sowie die Beurteilung der Wirkung von konservierender Bodenbearbeitung auf die Bodenabtrags- und Sedimentaustragsschätzungen. Es werden sowohl langandauernde advektive, zu Flusshochwasser führende Niederschlagsereignisse betrachtet als auch kurzzeitige konvektive Sommerereignisse, die nur zu wenig Abfluss oder aber auch zu Sturzfluten führen. Mit der entwickelten Methodik können saisonale und gebietsspezifische Eigenheiten wie Niederschlagscharakteristika, Landnutzung und Landbedeckung sowie Anfangsbodenfeuchte berücksichtigt werden. Ein Ergebnis ist die Bereitstellung von Eingangsdaten für die Optimierung der Steuerung von Hochwasserrückhaltebecken und Speichern zur gezielten Retention stofflicher Belastungen. Teile der Untersuchungen für diese Dissertation haben ihren Ursprung im RIMAX-Verbundvorhaben "Entwicklung eines integrativen Bewirtschaftungskonzepts für Trockenbecken und Polder zur Hochwasserrückhaltung".
Open Access
Investigations on functional relationships between cohesive sediment erosion and sediment characteristics
(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2021) Beckers, Felix; Wieprecht, Silke (Prof. Dr.-Ing.)
Open Access
Editorial for PFG issue 5/2023
(2023) Gerke, Markus; Cramer, Michael

Universität Stuttgart

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