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 The zero gravity curve and surface and radii for geostationary and geosynchronous satellite orbits(2017) Sjöberg, L. E.; Grafarend, Erik W.; Joud, M. S. S.A geosynchronous satellite orbits the Earth along a constant longitude. A special case is the geostationary satellite that is located at a constant position above the equator. The ideal position of a geostationary satellite is at the level of zero gravity, i.e. at the geocentric radius where the gravitational force of the Earth equals the centrifugal force. These forces must be compensated for several perturbing forces, in particular for the lunisolar tides. Considering that the gravity field of the Earth varies not only radially but also laterally, this study focuses on the variations of zero gravity not only on the equator (for geostationary satellites) but also for various latitudes. It is found that the radius of a geostationary satellite deviates from its mean value of 42164.2 km only within ±2 m, mainly due to the spherical harmonic coefficient J22, which is related with the equatorial flattening of the Earth. Away from the equator the zero gravity surface deviates from the ideal radius of a geosynchronous satellite, and more so for higher latitudes. While the radius of the former surface increases towards infinity towards the poles, the latter decreases about 520 m from the equator to the pole. Tidal effects vary these radii within ±2.3 km.Item Open Access "A la recherche du temps perdu" : on geological condensation, with examples from the Jurassic Subbetic Plateau in Southeastern Spain(1993) Fels, Alexander; Seyfried, HartmutEine kondensierte Ablagerung ist eine marine Rückstandsbildung, die sich autochthon und kontinuierlich über einen längeren Zeitraum hinweg gebildet hat. Kondensation kann synsedimentar durch Abschirmung, Ablenkung oder Abtragung von Schlamm erfolgen; diagenetisch läuft Kondensation vor allem über Bioerosion auf Hartboden in Verbindung mit unterschiedlich tiefgreifender Erosion ab. Im Jura des subbetischen Plateaus (früher "externes Subbetikum") wird das Umkippen in eine Kondensations-Situation durch tektonische Bewegungen gesteuert, indem benachbarte Plattformen den Export von Schlamm selbssteuernd regulieren. Das subbetische Plateau durchlief vier Phasen stark reduzierter Sedimentation: Carixien - Unter-Domerien(Kondensatlonsepisode 1), Obertoarcien - Unter-Bajocien (Kondensationsepisode 2), Unterbathonien - Unter-Oxfordien (Kondensationsepisode 3) und unterstes Kimmeridgien (Kondensationsepisode 4). Hartboden, die sich während dieser Episoden bildeten, sind meistens von Goethitkrusten und Goethitonkoiden bedeckt. Die meisten Goethitkrusten bestehen entweder aus sehr dünnen (20 - 50 p.) Laminae mit krümeligem und pseudofilamentösem Gefüge, die für einen mikrobiellen Ursprung der Krusten sprechen, oder sie bestehen aus strukturlosem, "sterilen" Goethiterz. Aus einer Abschätzung der Wachstumsraten folgern wir, daß der überwiegende Teil der Zeit, die in solchen Krusten versteckt ist, entweder im nur wenige Milimeter dicken Goethiterz dokumentlert ist oder in Hartböden bzw. Erosionsflächen gelöscht wurde. Kondensierte Abfolgen zelgen meistens eine hierarchische Gliederung in Sequenzen, die vom Meter- bis zum Millimeterbereich reichen und nahezu stets denselben Aufbau haben: sie beginnen mit reduzierter Sedimentation, auf die ein Omissionsstadium folgt und schließen melstens mlt Goethitkrusten ab. Unter den vielen Faktoren, die für eine solche Abfolge verantwortlich sein konnen, kristallisiert sich als kleinster gemeinsamer Nenner ein (nicht eindeutig kalibrierbares) eustatisches Signal heraus. Wir betrachten deshalb kondensierte Sequenzen, die Goethitkrusten enthalten, als pelagische Parasequenzen.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 Geospatial AI for heritage risk assessment : a machine learning approach to safeguarding cultural landmarks(2026) El-Deeb, Sara; Hussien, Osama; Fritsch, Dieter; Baraka, Moustafa Ahmed; Anwar, Mona B.Traditional heritage risk assessments rely on manual surveys and field inspections, which are often time-consuming and may fail to capture evolving risks comprehensively. In contrast, the emergence of geospatial big data presents new opportunities for leveraging artificial intelligence (AI) in heritage conservation. This study introduces an innovative methodology that integrates remote sensing data and machine learning to assess risks to heritage sites. Cairo, Egypt, with its vast and historically significant urban heritage, serves as the focal point of this analysis, as its cultural heritage (CH) faces increasing threats from rapid urbanization and development pressures. The proposed framework utilizes high-resolution satellite imagery and advanced geospatial analytics to systematically evaluate and prioritize vulnerable heritage sites. The analysis encompasses 9 districts, covering 1476 heritage sites, using a novel risk assessment framework that incorporates four key components: (1) urban development pressure measured through building density at multiple radii (50 m, 100 m, 200 m, 500 m); (2) heritage vulnerability assessment based on site age, area, and cultural significance; (3) isolation risk determined by surrounding building counts; and (4) environmental risk factors including material vulnerability, natural hazard exposure, emergency response limitations, and environmental pollution. Five machine learning models were evaluated using rigorous spatial cross-validation, with building density metrics at the 50 m radius emerging as the strongest independent predictors (r = 0.160, p < 0.001). By combining OpenStreetMap data, satellite imagery, and custom algorithms with methodologically rigorous feature selection to prevent data leakage, the study generates risk scores that facilitate data-driven decision-making for heritage preservation and sustainable tourism development. This study demonstrates how geospatial AI (GeoAI) can support CH preservation, and not just disaster prediction or urban analysis. The findings reveal that heritage risk is primarily driven by immediate urban context rather than site-specific characteristics, offering guidance for policymakers, UNESCO, NGOs, and developers in resource allocation.Item Open Access Spaceborne river discharge from a nonparametric stochastic quantile mapping function(2021) Elmi, Omid; Tourian, Mohammad J.; Bárdossy, András; Sneeuw, NicoThe number of active gauges with open‐data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground‐based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of parameters. Moreover, such methods do not deliver a proper estimation of the discharge's uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs. Here, we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: (a) generating realizations of river discharge and width time series using Monte Carlo simulation, (b) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, and (c) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po Rivers, and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodeling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.Item 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 A new magnetic anomaly map for Greenland based on a combination of equivalent source modeling and spherical harmonic expansion(2026) Heincke, Björn H.; Szwillus, Wolfgang; Freienstein, Judith; Ebbing, Jörg; Gaina, Carmen; Ruppel, Antonia; Dilixiati, Yixiati; Wansing, AgnesThe Greenland Magnetic Map (GREENMAG) is a new compilation of magnetic anomaly data that covers the inland ice, ice-free coastal areas, and adjacent shelf regions of Greenland ( 10.22008/FK2/LQN5YJ , Heincke and Szwillus, 2025). GREENMAG is based on all accessible modern regional aeromagnetic surveys from Greenland and vintage datasets without GPS positioning in areas where modern data are lacking. The magnetic anomaly map is generated by a combination of equivalent source (ES) modeling and spherical harmonic expansion. Hereby, the data points are used at their actual measurement location as input data for the inversion of the ES modeling. The equivalent sources are represented by magnetic dipoles that are arranged in three uniform grids with different source spacing and depths (coarsest spacing: 10 ×10 km; medium spacing: 2 ×2 km; finest spacing: 0.7 ×0.7 km). Regularization in the inversion for the different equivalent source grids are chosen such that the resulting resolution is adapted to the largely varying magnetic data coverage in Greenland. Since long wavelength components in aeromagnetic data are considered unreliable, they are replaced by the LCS-1 satellite model based on magnetic gradient measurements of the Swarm and CHAMP missions. For merging, the responses from the individual equivalent dipole sources are transferred to spherical harmonics and replaced for degree n=13-133 by the Gaussian coefficients of the LCS-1 model. The final magnetic anomaly map is calculated from the combined model at a constant height of 2000 m a.s.l. (WGS84) and with a grid spacing of 400 ×400 m. The comparison between the GREENMAG and the earlier compilation from the Circum-Arctic Mapping Project (CAMP-M) highlights the enhanced level of detail now available across many regions of Greenland.Item Open Access The potential of EO data for enhanced flood monitoring and forecasting : a consortium assessment(2026) Tarpanelli, Angelica; Massari, Christian; Revilla-Romero, Beatriz; Tourian, Mohammad J.; Saemian, Peyman; Elmi, Omid; Scherer, Daniel; Pedinotti, Vanessa; Kittel, Cecile; Benveniste, Jérôme; Bauer-Gottwein, Peter; Ciabatta, Luca; Chewning, Connor; Barbetta, Silvia; Filippucci, Paolo; Cantoni, Èlia; Dettmering, Denise; Andersson, Jafet; Gal, Laetitia; Gustafsson, David; Hundecha, Yeshewatesfa; Larnicol, Gilles; Larnier, Kevin; Nielsen, Karina; Paris, Adrien; Sadki, Malak; Schwatke, Christian; Tamagnone, Paolo; Vrettou, Artemis; Douch, Karim; Volden, Espen; Schumann, GuyThe monitoring and modeling of riverine floods have been covered extensively in the scientific literature with a substantial number of scientific contributions related to calibration/validation of hydraulic and hydrological models and assimilation of Earth Observation (EO) data into them. These models, when used for flood forecasting purposes, rely heavily on ground-based hydrological networks along with numerical weather models which, particularly in data-scarce regions, are often challenged by data sparsity. In these situations, EO data offer a viable solution to enhance the skill of these flood forecasting systems by providing global-scale observations of key hydrological variables such as precipitation, soil moisture, river discharge, water levels, and flood extent. This manuscript reviews and discusses the capability of these EO data in enhancing flood forecasting systems, by analyzing their accuracy, lead time, and reliability, while at the same time highlighting key challenges such as data latency, spatial–temporal resolution trade-offs, and model assimilation constraints. By leveraging recent advancements in remote sensing, data assimilation techniques, and artificial intelligence, EO-based flood forecasting has the potential to bridge existing observational gaps, particularly in vulnerable regions. The paper also outlines future research directions and technological developments needed to maximize the impact of satellite data in operational flood forecasting systems.Item 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, XiaoyangWater 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.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.