EGU22-692
https://doi.org/10.5194/egusphere-egu22-692
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Autonomous lineament detection in Galileo images of Europa

Caroline Haslebacher1 and Nicolas Thomas2
Caroline Haslebacher and Nicolas Thomas
  • 1Physics Institute, University of Bern, Bern, Switzerland (caroline.haslebacher@unibe.ch)
  • 2Physics Institute, University of Bern, Bern, Switzerland (nicolas.thomas@unibe.ch)

Lineaments are prominent features on the surface of Jupiter's moon Europa. Analysing these linear features thoroughly leads to insights on their formation mechanisms and the interactions between the subsurface ocean and the surface. The orientation and position of lineaments is also important for determining the stress field on Europa. The Europa Clipper mission is planned to launch in 2024 and will fly by Europa more than 40 times. In the light of this, an autonomous lineament detection and segmentation tool would prove useful for processing the vast amount of expected images efficiently and would help to identify processes affecting the ice sheet. 

We have trained a convolutional neural network to detect, classify and segment lineaments in images of Europa returned by the Galileo mission. The Galileo images that make up the training set are segmented manually, following a dedicated guideline. For better performance, we make use of synthetically generated data to pre-train the network. The current status of the work will be described.

How to cite: Haslebacher, C. and Thomas, N.: Autonomous lineament detection in Galileo images of Europa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-692, https://doi.org/10.5194/egusphere-egu22-692, 2022.

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