ICG2022-460, updated on 14 Sep 2023
10th International Conference on Geomorphology
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Automatic geological structure recognition at the Dead Sea lakebed

Osama AlRabayah1, Djamil Al-Halbouni1, Robert A. Watson2, Danu Caus3, Harsh Grover3, David Nakath4, Lars Rüpke1, and Tobias Weigel3
Osama AlRabayah et al.
  • 1Dynamics of the Ocean Floor, Seafloor Modelling Group, GEOMAR Helmholtz Centre for Ocean Research, Wischhofstrasse 1–3, 24148 Kiel, Germany
  • 2UCD School of Earth Sciences, University College Dublin, Belfield, Dublin, Ireland
  • 3Applications Department, DKRZ, Bundesstraße 45a, 20146 Hamburg; Helmholtz AI, Ingolstädter Landstraße 1, 85764 Oberschleißheim
  • 4Ocean Machine Vision Group, GEOMAR Helmholtz Centre for Ocean Research, Düsternbrooker Weg 20, 24105 Kiel, Germany

This research aims at developing and applying a machine learning based algorithm to detect geological structural features at the exposed Dead Sea shoreline. We focus on sinkhole, stream-channel and crack features that appear in different material at the coastlines, and post partly a threat for the local population and infrastructure. We use high resolution orthophotos and satellite images from the last years, as well as derived topographic models to reach an automatic identification and classification of these structures. The aim is to train a convolutional neural network that can identify these structures on recent datasets from satellite images in order to establish an automated detection of hazardous and active zones in the area. Furthermore, we use the algorithms to detect structures in the shallow waters of the lake.

How to cite: AlRabayah, O., Al-Halbouni, D., Watson, R. A., Caus, D., Grover, H., Nakath, D., Rüpke, L., and Weigel, T.: Automatic geological structure recognition at the Dead Sea lakebed, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-460, https://doi.org/10.5194/icg2022-460, 2022.