06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7
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Item Open Access Editorial for PFG issue 5/2023(2023) Gerke, Markus; Cramer, MichaelItem Open Access Geospatial information research : state of the art, case studies and future perspectives(2022) Bill, Ralf; Blankenbach, Jörg; Breunig, Martin; Haunert, Jan-Henrik; Heipke, Christian; Herle, Stefan; Maas, Hans-Gerd; Mayer, Helmut; Meng, Liqui; Rottensteiner, Franz; Schiewe, Jochen; Sester, Monika; Sörgel, Uwe; Werner, MartinGeospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors - members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany - have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future.Item Open Access Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics(2024) Long, Qingqing; Zhang, Xinlong; Ren, Fangyuan; Wu, Xinyu; Wang, Ze-MuIntroduction: Heart failure (HF) and kidney failure (KF) are closely related conditions that often coexist, posing a complex clinical challenge. Understanding the shared mechanisms between these two conditions is crucial for developing effective therapies. Methods: This study employed transcriptomic analysis to unveil molecular signatures and novel biomarkers for both HF and KF. A total of 2869 shared differentially expressed genes (DEGs) were identified in patients with HF and KF compared to healthy controls. Functional enrichment analysis was performed to explore the common mechanisms underlying these conditions. A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. These genes were further analyzed using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA), with their diagnostic values validated in both training and validation sets. Molecular docking studies were conducted. Additionally, immune cell infiltration and correlation analyses were performed to assess the relationship between immune responses and the identified biomarkers. Results: The functional enrichment analysis indicated that the common mechanisms are associated with cellular homeostasis, cell communication, cellular replication, inflammation, and extracellular matrix (ECM) production, with the PI3K-Akt signaling pathway being notably enriched. The PPI network revealed two key protein clusters related to the cell cycle and inflammation. CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. Additionally, docking studies with CDK2 and CCND1 were performed to evaluate potential drug candidates. Immune cell infiltration and correlation analyses highlighted the immune microenvironment, and that CDK2 and CCND1 are associated with immune responses in HF and KF. Discussion: This study identifies CDK2 and CCND1 as novel biomarkers linking cell cycle regulation and inflammation in heart and kidney failure. These findings offer new insights into the molecular mechanisms of HF and KF and present potential targets for diagnosis and therapy.Item Open Access Measuring the wisdom of the crowd : how many is enough?(2022) Walter, Volker; Kölle, Michael; Collmar, DavidThe idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection.Item Open Access Orientierung im Raum - 200 Jahre Maschine von Bohnenberger : Ausstellung im Foyer Universitätsbibliothek Stuttgart, 10. Dezember 2010 bis 29. Januar 2011(Stuttgart: Universität Stuttgart, Organisationsteam Bohnenberger, 2010) Hügel, Hubert; Fritsch, Dieter; Wagner, Jörg; Brullo, Giovanni; Hügel, Hubert; Eickholt, Mechthild (Grafik)Die Broschüre dokumentiert die Ausstellung in der Universitätsbibliothek Stuttgart, die aus Anlass des Jubiläums "200 Jahre Maschine von Bohnenberger" vom 10. Dezember 2010 bis 29. Januar 2011 zu sehen war.Item Open Access An improved tree crown delineation method based on a gradient feature-driven expansion process using airborne LiDAR data(2025) Jia, Jiaxuan; Zhang, Lei; Yin, Kai; Sörgel, UweAccurate individual tree crown delineation (ITCD), which can be used to estimate various forest parameters such as biomass, stem density, and carbon storage, stands as an essential component of precision forestry. Currently, raster data such as the canopy height model derived from airborne light detection and ranging (LiDAR) data have been widely used in large-scale ITCD. However, the accuracy of current existing algorithms is limited due to the influence of understory vegetation and variations in tree crown geometry (e.g., the delineated crown boundaries consistently extend beyond their actual boundaries). In this study, we achieved more accurate crown delineation results based on an expansion process. First, the initial crown boundaries were extracted through watershed segmentation. Then, a “from the inside out” expansion process was guided by a novel gradient feature to obtain accurate crown delineation results across different forest conditions. Results show that our method produced much better performance (~75% matched on average) than other commonly used methods across all test forest plots. The erroneous situation of “match but over-grow” is significantly reduced, regardless of forest conditions. Compared to other methods, our method demonstrates a notable increase in the precisely matched rate across different plot types, with an average increase of 25% in broadleaf plots, 18% in coniferous plots, 23% in mixed plots, 15% in high-density plots, and 32% in medium-density plots, without increasing over- and under- segmentation errors. Our method demonstrates potential applicability across various forest conditions, facilitating future large-scale ITCD tasks and precision forestry applications.Item Open Access Building a fully-automatized active learning framework for the semantic segmentation of geospatial 3D point clouds(2024) Kölle, Michael; Walter, Volker; Sörgel, UweIn recent years, significant progress has been made in developing supervised Machine Learning (ML) systems like Convolutional Neural Networks. However, it’s crucial to recognize that the performance of these systems heavily relies on the quality of labeled training data. To address this, we propose a shift in focus towards developing sustainable methods of acquiring such data instead of solely building new classifiers in the ever-evolving ML field. Specifically, in the geospatial domain, the process of generating training data for ML systems has been largely neglected in research. Traditionally, experts have been burdened with the laborious task of labeling, which is not only time-consuming but also inefficient. In our system for the semantic interpretation of Airborne Laser Scanning point clouds, we break with this convention and completely remove labeling obligations from domain experts who have completed special training in geosciences and instead adopt a hybrid intelligence approach. This involves active and iterative collaboration between the ML model and humans through Active Learning, which identifies the most critical samples justifying manual inspection. Only these samples (typically ≪1%of Passive Learning training points) are subject to human annotation. To carry out this annotation, we choose to outsource the task to a large group of non-specialists, referred to as the crowd, which comes with the inherent challenge of guiding those inexperienced annotators (i.e., “short-term employees”) to still produce labels of sufficient quality. However, we acknowledge that attracting enough volunteers for crowdsourcing campaigns can be challenging due to the tedious nature of labeling tasks. To address this, we propose employing paid crowdsourcing and providing monetary incentives to crowdworkers. This approach ensures access to a vast pool of prospective workers through respective platforms, ensuring timely completion of jobs. Effectively, crowdworkers become human processing units in our hybrid intelligence system mirroring the functionality of electronic processing units .Item Open Access Visual navigation for lunar missions using sequential triangulation technique(2025) Muratoglu, Abdurrahim; Söken, Halil Ersin; Tekinalp, OzanA vision-aided autonomous navigation system for translunar missions based on celestial triangulation (Earth and Moon) is proposed. Line-of-Sight (LoS) vectors from the spacecraft to celestial bodies, retrieved using ephemeris data from the designed translunar trajectory, are used to simulate camera observations at unknown locations. The resection problem of triangulation is employed to calculate the relative position of the spacecraft with respect to the observed bodies along the trajectory. The noisy LoS data are processed using the Extended Kalman Filter (EKF). Simulation results demonstrate that, starting from a random initial location, the proposed navigation system can be used for navigating translunar trajectories with the fast and accurate algorithm employed.Item Open Access Error covariance analyses for celestial triangulation and its optimality : improved linear optimal sine triangulation(2025) Muratoglu, Abdurrahim; Söken, Halil Ersin; Soergel, UweThis study presents an improved methodology for celestial triangulation optimization in spacecraft navigation, addressing limitations in existing approaches. While current methods like Linear Optimal Sine Triangulation (LOST) provide statistically optimal solutions for position estimation using multiple celestial body observations, their performance can be compromised by suboptimal measurement pair selection. The proposed approach, called the Improved-LOST algorithm, introduces a systematic method for evaluating and selecting optimal measurement pairs based on a Cramér-Rao Lower-Bound (CRLB) analysis. Through theoretical analysis and numerical simulations on translunar trajectories, this study demonstrates that geometric configuration significantly influences position estimation accuracy, with error variances varying by orders of magnitude depending on observation geometry. The improved algorithm outperforms conventional implementations, particularly in scenarios with challenging geometric configurations. Simulation results along a translunar trajectory using various celestial body combinations show that the systematic selection of measurement pairs based on CRLB minimization leads to enhanced estimation accuracy compared to arbitrary pair selection. The findings provide valuable insights for autonomous navigation system design and mission planning, offering a quantitative framework for assessing and optimizing celestial triangulation performance in deep space missions.