GGOS study group of "AI for Geodetic Deformation Monitoring"

Aktivität: Teilnahme an oder Organisation einer VeranstaltungOrganisation einer beruflichen Weiterbildung

Personen

  • Mohammad Ali Sharifi (Vorsitz)
  • Mohammad Omidalizarandi (Co-Vorsitz)

Organisationseinheiten

Forschungs-netzwerk anzeigen

Organisation einer beruflichen Weiterbildung

Veranstaltung

Name der VeranstaltungGGOS study group of "AI for Geodetic Deformation Monitoring"
Datum22 Sept. 2023 → …
Webseite
OrtOnline
Ort
BekanntheitsgradInternationale Veranstaltung
Datum

22 Sept. 2023 → …

Beschreibung

Monitoring the system Earth and man-made structures, and their deformation induced by natural or anthropogenic forces is recognized as a key role of modern geodesy. Different space and terrestrial geodetic technologies have been employed for precise measurement and identification of spatio-temporal deformation of the earth surface. The geodetic measurement techniques are getting even more precise with unprecedented temporal and spatial resolutions. For example, Dense GNSS Continuously Operating Reference Stations (CORS) and the Interferometric Synthetic Aperture Radar (InSAR) with complementary abilities successfully monitor the earth system dynamics. Nonlinearity and complexity of deformation patterns on the one hand and the need for knowledge mining in the steadily growing big geodetic data on the other hand make use of machine learning and AI-assisted approaches vital to the geodetic community.

Objectives:
- Automatic recognition and identification of spatio-temporal patterns associated to various deformation mechanisms such as tectonics, volcanism, land subsidence, ice and glacier motion, and landslides in InSAR data.
- Detection of abnormalities and sinkhole-precursors signals in deformation time series acquired by different geodetic techniques (e.g., InSAR, GPS, Levelling, etc).
- AI potential for mitigation of atmospheric effects on InSAR and GNSS data through AI training on meteorological models.
- Machine learning-based classification of deformation time series based on different kinematic patterns.
- Estimation and calibration of civil structure dynamic models by exploiting AI learning capabilities.
- Deep-learning-based structural health monitoring of civil infrastructures and buildings using satellite or ground-based geodetic measurements.
- Automatic detection of offsets, probable slow slip events, and abnormal earthquake patterns in GNSS time series.
- Identification of complex multi-fault ruptures and triggering mechanisms using local CORS networks.
- Development of machine learning methodologies to forecast how deformations might evolve in the future.