GSA Connects 2021 in Portland, Oregon

Paper No. 109-4
Presentation Time: 2:20 PM

TOWARDS AUTOMATIC DETECTION OF SHATTER CONES ON PLANETARY ROVER IMAGES: FIRST RESULTS OF A FEASIBILITY STUDY (Invited Presentation)


BECHTOLD, Andreas1, PAAR, Gerhard2, TRAXLER, Christoph3, NOWAK, Rebecca3, GAROLLA, Filippo4 and KOEBERL, Christian, PhD5, (1)Department of Lithospheric Research, University Vienna, UZA 2, Althanstrasse 14, Vienna, 1020, Austria, (2)Joanneum Research, Steyrergasse 17, Graz, 8010, Austria, (3)VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Donau-City-Strasse 1, Vienna, 1220, Austria, (4)SLR Engineering GmbH, Gartengasse 19, Graz, 8010, Austria, (5)Department of Lithospheric Research, University of Vienna, Althanstrasse 14, Vienna, A-1180, Austria

The exploration of other planetary bodies, both by remote investigations and in situ, has fundamentally contributed to our understanding of impact craters in our solar system. On Earth, shatter cones represent the only unambiguous macroscopic evidence of shock metamorphism (e.g., French and Koeberl 2010). These features have not been observed on the surface of any other planetary body, or on the Moon, even though a few rare occurrences were noted in meteorites (Ferrière et al, 2013). Planetary surface rover mission as, for example, the current Mars 2020 mission, provide huge amounts of images over years. Due to various reasons, these images cannot always be evaluated in a timely manner, which raises the demand for automatic methods on ground, but in future also to improve rover science autonomy.

The Mars-DL project focused on the automatic detection of shatter cones in Martian image scenes. Machine learning methods trained a convolutional neural network (CNN) to detect shatter cones in Mars rover images acting as a “scientific target consultant” (STC). As such images do not exist in reality, we assembled training images using virtual shatter cones, 3D-digitized from real terrestrial specimens provided by the Natural History Museum Vienna. For the CNN training at least a few thousand annotated images are necessary which was solved by image rendering of 3D scenes obtained from MSL Mastcam (Malin et al, 2010) stereo sequences. One challenge was to create realistic looking sceneries containing several artificially placed virtual shatter cones. We tested the preliminary STC version with additional non-annotated artificial images and terrestrial desert scenes from Ethiopia, which contained manually placed (real) shatter cones. These initial tests showed promising results, but also demonstrated the difficulties in achieving a reliable detection rate.

Funding: Austrian ASAP14 Programme.

Ferrière L., Brandstätter F. and Koeberl C. (2013) Finding Shatter Cones in Meteorites from the Natural History Museum Vienna Collection. 76. Met. Soc. Mtg., Abs. #5193.

French B.M. & Koeberl C. (2010) The convincing identification of terrestrial meteorite impact structures. Earth-Sci. Rev. 98, 123–170.

Malin M. C. et al. (2010) The Mars Science Laboratory (MSL) mast-mounted cameras (Mastcams) flight instruments. LPSC. Abs. #1533.