Geodetic Institute
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Projects | TLS-based Multi-Sensor-Systems

  • Deformation analysis based on terrestrial laser scanner measurements (TLS-Defo): Surface approximation uncertainty
    In geodetic deformation analyses, we statistically test geometric changes in two or more states. To use the full potential of well-established surface-based measurement techniques such as terrestrial laser scanning (TLS) requires continuous local and global modeling of the monitored surface. The project on "surface approximation uncertainty" focuses on investigating the interaction between measurement and model uncertainties in the context of surface model selection. These components are closely connected, as the level of model uncertainty is directly influenced by the interaction between the complexity of the measured object, such as roughness and sharp edges, and the spatial density of measurement points across the object. To address this, the project distinguishes between three sub-topics: TLS uncertainty budget, model uncertainty, and the application of fractal geometry as a methodological tool to achieve the primary project goal.
    Led by: Ingo Neumann, Mohammad Omidalizarandi
    Team: Jan Hartmann
    Year: 2023
    Funding: DFG
    Duration: 10/23 – 09/27

Projects | Expert-based data analysis and quality processes

  • Efficiency optimization of geodetic measurement processes
    The efficiency optimization of measurement and evaluation processes of engineering geodesy requires a detailed modeling of the individual sub-steps. This modeling is realized by means of Petri nets. Thus computer-aided simulations can be carried out. To minimize the cost or duration of the measurement processes Genetic algorithms are used as an optimization method.
    Led by: Hansjörg Kutterer
    Team: Ilka von Gösseln
    Year: 2009
    Funding: DFG
    Duration: 05/2009 - 06/2014
  • Risk Minimization in Structural Safty Monitoring
    One main goal of structural safety monitoring is minimizing the risk of un-expected collapses of artificial objects and geologic hazards. Behind these activities in the DFG founded project, it is the need of the society in mini-mizing the negative environmental impacts. An optimal configuration for measurement setups and all other decisions shall therefore review and ra-te the risks of an individual monitoring project. Nowadays, the methodolo-gy in many engineering disciplines and mathematically founded decisions are usually based on probabilities and significance levels but not on the risk (consequences or costs) itself.
    Led by: Ingo Neumann
    Team: Yin Zhang
    Year: 2010
    Funding: DFG
    Duration: 09/2011 - 08/2014
  • Simulation-based optimization of tachymetric network measurements
    In geodetic networks of large extent or with a large number of points, the tachymetric network measurement is usually associated with a high logistical effort. The individual measuring points must be visited again and again in order to align the reflectors to the current tachymeter position. The efficient planning of the measurement has the goal of causing the lowest possible costs or it aims at the shortest possible measuring duration.
    Team: Ilka von Gösseln
    Year: 2010
    Duration: 2010 - 2019

Projects | Interdisciplinary Monitoring

  • OpenData4InfMon: Monitoring with GNSS sensors and open data
    The ageing infrastructure on land, rail and water requires significant resources to ensure operational safety. The monitoring of deformations, especially on bridge structures and other important infrastructure, caused by ageing, material fatigue and slow (also climate-related) ground movements, is currently very cost-intensive. It is therefore necessary to develop and evaluate mass-applicable and cost-efficient analysis methods based on open data sources combined with local GNSS sensors, which do not yet exist. The project will investigate the possibilities of strict fusion of free GNSS and radar data as well as 3D city models and traffic route plans for the purpose of better assessment of deformations on structures in combination with locally installed sensor technology, in particular on infrastructures such as railroad lines, power lines and (bridge) structures. The added value of the data is generated in particular by AI analyses and spatiotemporal parameter estimation in combination with local GNSS data.
    Led by: Ingo Neumann, Mohammad Omidalizarandi
    Team: Kourosh Shahryarinia
    Year: 2023
    Funding: Bundesministerium für Digitales und Verkehr (BMDV)
    Duration: 03/23 – 08/24