Leibniz Universität Hannover Faculty Faculty of Civil Engineering and Geodetic Science
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Geodetic Institute
 
Geodetic Institute Hanover Research Research Projects

Projects | TLS-based Multi-Sensor-Systems

  • Monitoring a track warping test
    GIH was responsible for the metrological monitoring of a very unusual test conducted by Deutsche Bahn - a track warping test. A previously selected rail area was heated to a very high temperature, which initially caused the rail to deform and then warp. This track warping test took place in June 2017 in southern Lower Saxony.
    Led by: Ingo Neumann, Jens-André Paffenholz
    Team: Johannes Bureick, Ilka von Gösseln, Dmitri Diener
    Year: 2017
    Duration: 05/2017 - 12/2017
    © GIH / U. Stenz
  • Quality control of building components using quadruped robots in challeging enviroments
    The aim of this postdoctoral project, which was carried out as part of the DFG-funded Research Training Group i.c.sens, was to establish the methodological foundations for the autonomous navigation of a quadruped robot, as this is an essential prerequisite for robot-assisted quality inspection in construction environments.
    Led by: Prof. Dr.-Ing. Ingo Neumann, PD Dr.-Ing. Hamza Alkhatib
    Team: Dr.-Ing. Rozhin Moftizadeh
    Year: 2022
    Funding: DFG - GRK 2159 i.c.sens until November 2025
    Duration: 11/2022 - a.w.
    © GIH
  • AutoMap - Development of a robust positioning system for autonomous vehicles based on collected environmental information and GNSS/IMU data
    Determining the exact position of vehicles is not only crucial for autonomous driving, but also for many other applications. However, existing technologies, such as global navigation satellite systems (GNSS) or inertial measurement units (IMU), are reaching their limits due to interference and inaccuracies, especially in urban areas.
    Led by: PD Dr.-Ing. Hamza Alkhatib
    Team: Mohamad Wahbah, M. Sc., Dr.-Ing. Rozhin Moftizadeh
    Year: 2023
    Funding: mFUND project | funded by the BMDV (Bundesministerium für Digitales und Verkehr)
    Duration: 2023-2025
    © GIH
  • Deformation analysis based on terrestrial laser scanner measurements (TLS-Defo, FOR 5455): Uncertainty of the surface approximation
    Geodetic deformation analysis involves the statistical analysis of geometric changes in two or more states. To exploit the full potential of established surface-based measurement techniques, such as terrestrial laser scanning (TLS), continuous local and global modelling of the monitored surface is required. The project ‘Uncertainty of Surface Approximation’ focuses on the investigation of the interaction between measurement and model uncertainties in the context of surface model selection. These components are closely related, since the amount 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 over the object. To address this, the project differentiates between three subtopics: 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
  • Measurement system analysis and model-based sensor fusion for hydrographic water exchange zone monitoring using unmanned carrier systems
    The aim of the project "Measurement system analysis and model-based sensor fusion for hydrographic water exchange zone monitoring with unmanned carrier systems (WaMUT)", which the GIH is working on on behalf of and in cooperation with the Bundesanstalt für Gewässerkunde (BfG), is the continuous, quality-assured acquisition and modelling of geo-base data of the water exchange zones and shallow water areas of federal waterways to improve the quality of - in particular small-scale - digital terrain models of the watercourse. In contrast to the classic geodetic observation methods for recording bathymetry and topography, the use of unmanned sensor platforms - primarily on land (unmanned aerial vehicle UAV), but also on the water (unmanned surface vessel USV) - has come into focus in recent years. As part of the WaMUT project, these measurement systems are to be validated and, based on this, a quality-assured, integrated measurement programme is to be created in order to be able to record reliable geobase data in the water exchange zones.
    Led by: Prof. Dr.-Ing. Ingo Neumann, PD Dr.-Ing. Hamza Alkhatib
    Team: Bahareh Mohammadivojdan, Frederic Hake
    Year: 2020
    Duration: 09/2020 - 08/2024
    © BfG
  • port_AI – A fully digital twin for port structures using IoT, 5G, BIM, AR and AI methods to establish smart building lifecycle management
    The requirements for the safety and reliability of infrastructure management of infrastructure in the area of sea and inland ports are constantly increasing due to the growing globalisation of trade. The creation of a smart infrastructure should solve various challenges in the management of existing port infrastructure in this project. Digitisation and the use of AI processes are also included in this project under the term smart infrastructure. Only a thoroughly digital management of port infrastructure enables the economical use of resources, forward-looking maintenance, and early and comprehensive damage detection and assessment. This can lead to significant cost savings.
    Led by: Ingo Neumann, Hamza Alkhatib, Mohammad Omidalizarandi
    Team: Arshia Shisheh Garan, Frederic Hake
    Year: 2021
    Funding: Funding programme for innovative port technologies (IHATEC) supported by the Federal Ministry of Transport and Digital Infrastructure (BMVI)
    Duration: 12/2021 – 02/2025
  • Uncertainty Modeling for Kinematic LiDAR-based Multi-Sensor Systems
    Goal of this PhD project is to investigate methods to enable a consistent estimation of uncertainties for LiDAR-based MSSs, while dealing with the challenges caused by the uncertainties of individual sensors and their interactions in the system.
    Led by: Prof. Dr.-Ing. Ingo Neumann
    Team: Dominik Ernst, M. Sc.
    Year: 2022
    Funding: DFG - GRK 2159 i.c.sens until November 2025
    Duration: 11/2022 - until further notice
    © GIH | Dominik Ernst
  • Development of a collaborative robust Particle Filter for State Estimation with Stochastic and Quantity-based Uncertainties in Sensor Networks
    Precise vehicle localization is a critical requirement for autonomous driving, especially in urban settings where GNSS signals often fail. To address this challenge, an advanced Particle Filter framework estimates vehicle pose by fusing 3D LiDAR data with complementary sensor inputs. The primary motivation is to achieve low-decimetre localisation accuracy despite the complexities of urban environments.
    Led by: PD Dr.-Ing. Hamza Alkahtib
    Team: Marvin Scherff, M. Sc.
    Year: 2022
    Funding: DFG - GRK 2159 i.c.sens until November 2025
    Duration: 11/2022 - until further notice
  • AutoMap - Development of a robust positioning system for autonomous vehicles based on collected environmental information and GNSS/IMU data
    Determining the exact position of vehicles is not only crucial for autonomous driving, but also for many other applications. However, existing technologies, such as global navigation satellite systems (GNSS) or inertial measurement units (IMU), are reaching their limits due to interference and inaccuracies, especially in urban areas.
    Led by: PD Dr.-Ing. Hamza Alkhatib
    Team: Mohamad Wahbah, M. Sc., Dr.-Ing. Rozhin Moftizadeh
    Year: 2023
    Funding: mFUND project | funded by the BMDV (Bundesministerium für Digitales und Verkehr)
    Duration: 2023-2025
    © GIH

Projects | Interdisciplinary Monitoring

  • port_AI – A fully digital twin for port structures using IoT, 5G, BIM, AR and AI methods to establish smart building lifecycle management
    The requirements for the safety and reliability of infrastructure management of infrastructure in the area of sea and inland ports are constantly increasing due to the growing globalisation of trade. The creation of a smart infrastructure should solve various challenges in the management of existing port infrastructure in this project. Digitisation and the use of AI processes are also included in this project under the term smart infrastructure. Only a thoroughly digital management of port infrastructure enables the economical use of resources, forward-looking maintenance, and early and comprehensive damage detection and assessment. This can lead to significant cost savings.
    Led by: Ingo Neumann, Hamza Alkhatib, Mohammad Omidalizarandi
    Team: Arshia Shisheh Garan, Frederic Hake
    Year: 2021
    Funding: Funding programme for innovative port technologies (IHATEC) supported by the Federal Ministry of Transport and Digital Infrastructure (BMVI)
    Duration: 12/2021 – 02/2025
  • 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: Prof. Dr.-Ing. Ingo Neumann, Dr.-Ing. Mohammad Omidalizarandi
    Team: Kourosh Shahryarinia, M. Sc.
    Year: 2023
    Funding: Bundesministerium für Digitales und Verkehr (BMDV)
    Duration: 03/2023 – 08/2024
  • Deformation analysis based on terrestrial laser scanner measurements (TLS-Defo, FOR 5455): Uncertainty of the surface approximation
    Geodetic deformation analysis involves the statistical analysis of geometric changes in two or more states. To exploit the full potential of established surface-based measurement techniques, such as terrestrial laser scanning (TLS), continuous local and global modelling of the monitored surface is required. The project ‘Uncertainty of Surface Approximation’ focuses on the investigation of the interaction between measurement and model uncertainties in the context of surface model selection. These components are closely related, since the amount 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 over the object. To address this, the project differentiates between three subtopics: 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
  • Large-Scale InSAR Deformation Monitoring Using Realistic Simulation-Based Training of a Deep Learning Model
    Large-scale land surface deformation monitoring using Interferometric Synthetic Aperture Radar (InSAR) requires robust detection of changes in long-term deformation trends. However, accurate change point (CP) detection remains challenging due to the complex characteristics of InSAR time series, including seasonal and quasi-periodic components, as well as noise. Classical statistical methods and many existing deep learning approaches rely on restrictive assumptions or training data that do not fully represent real-world InSAR time series, resulting in limited generalization capability and scalability for large-scale operational applications. This study focuses on the use of deep learning models to address these challenges.
    Led by: Prof. Dr.-Ing. Ingo Neumann, Dr.-Ing. Mohammad Omidalizarandi
    Team: Kourosh Shahryarinia, M. Sc.
    Year: 2024
    Funding: DAAD Research Grant
    Duration: 10/2024 - 09/2027
    © GIH

Projects | Land and Real Estate Management

  • InDaLE - Innovative Approaches to Services of General Interest in Rural Areas- What Germany can learn from the experiences of other European countries
    In structurally weak and sparsely populated rural regions in particular, the consequences of demographic change and financially weak municipalities are threatening the existence of certain public services. This makes it increasingly difficult to maintain public services in the long term. Other European countries find themselves in a similar situation, in some cases with significantly lower population densities. This is where the project comes in, to examine the extent to which there are established, innovative examples of public service provision in these countries that can offer additional insights and solutions for Germany.
    Led by: Prof. Dr.-Ing. Winrich Voß
    Team: Dr.-Ing. Jörn Bannert, Alice Gebauer
    Year: 2020
    Funding: BMLEH - Federal Ministry of Agriculture, Food and Regional Identity
    Duration: 2020 - 2022

Last Change: 27.03.26 Print

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