Current projects

Projects | Interdisciplinary Monitoring

• 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
• 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