Chair: Mohammad Ali Sharifi (Iran), sharifi@ut.ac.ir

Vice-Chair: vacant

General Contact: ai4deformation (at) ggos.org

Terms of Reference (ToR) / Description: 

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.

Members:

1. Mohammad Ali Sharifi (University of Tehran, Iran, Sharifi@ut.ac.ir)
2. Ingo Neumann (Geodetic Institute, Leibniz University Hannover, Germany, neumann@gih.uni-hannover.de)
3. Alireza Amiri Simkooei (Technical University of Delft, Netherland, amirisimkooei@gmail.com)
4. Andrew Hooper (University of Leeds, England, Hooper@leeds.ac.uk)
5. Amir M. Abolghasem (Ludwig-Maximilians University, Germany, amir.a@lmu.de )
6. Sami Samiei Esfahany (University of Tehran, Iran, SamieiEsfahany@ut.ac.ir)
7. Zhiping Chen (East China University of Technology, China, zhpchen@ecut.edu.cn)
8. Xiaoxing He (Jiangxi University of Science and Technology, China, xxh@jxust.edu.cn)
9. Roland Hohensinn (International Space Science Institute, Switzerland, hohensinn@issibern.ch)
10. Yao Yevenyo Ziggah (University of Mines and Technology, Ghana, yyziggah@umat.edu.gh)
11. Dimitrios Anastasiou (National Technical University of Athens, Greece, danastasiou@mail.ntua.gr)
12. Stylianos Bitharis (Aristotle University of Thessaloniki, Greece, smpithar@topo.auth.gr)
13. Liming Jiang (State Key Laboratory of Geodesy and Earth’s Dynamics Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, China, jlm@apm.ac.cn)
14. Shunqiang Hu (Jiangxi Normal University, China, husq@jxnu.edu.cn)