Planetary Surface Feature Detection Using Machine Learning

<p>Recent technological advances have enabled satellites orbiting planetary bodies to retrieve more abundant streams of data faster. Machine Learning (ML) and other computer vision techniques provide the opportunity to analyse such data with higher accuracies and within shorter time-scales – much shorter than any human can achieve. However, the use of ML in planetary science is not accelerating at the same rate as in the related fields of geophysics or astronomy [1].</p> <p>In this presentation we will present progress in creating ML tools for planetary surface feature detection. The goal of these tools is to fully exploit previously untapped volumes of available space data.</p> <p>We will present an automated tool called the Martian Pit Shadow extractor (MAPS) which can detect the shadows cast by Martian pits and calculate their apparent depths. We will also present the results of applying MAPS to a current feature catalogue in the Mars Global Cave Candidate Catalog (MGC<sup>3</sup>) [2]. Pits are circular-to-elliptical depressions on the surface of terrestrial planets, which are most likely caused by gravitational collapse into a sub-surface void. These features are potential entrances to underground networks of evacuated lava tubes [3] and the depth of the pit is a factor that will influence the volume of the intact lava tube [4]. The propensity for ice caves to exist on Mars also increases with the thickness of the cavity’s ceiling [5], which itself will be larger for deeper pits. The purpose of MAPS is to automatically extract the shadow from a single cropped image of a Martian pit, as a means of calculating its apparent depth without the need for corresponding stereo images or elevation data. The apparent depth is defined as the depth of the pit at the extent of its shadow along the Sun’s line of sight [6]. MAPS has been tested with several methods of image segmentation with varying degrees of complexity from watershed transformation up to K-Means clustering. The next step is to adapt MAPS to other datasets to analyse pits on other planetary bodies such as the Moon.</p> <p>Acknowledgements: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101004214.</p> <p>[1] Azari, A.R.; Biersteker, J.B.; Dewey, R.M.; Doran, G.; Forsberg, E.J.; Harris, C.D.; Kerner, H.R.; Skinner, K.A.; Smith, A.W.; Amini, R.; Cambioni, S. Integrating machine learning for planetary science: Perspectives for the next decade, 2020, arXiv:2007.15129.</p> <p>[2] Cushing, G.E. Mars Global Cave Candidate Catalog, PDS4 Archive Bundle, PDS Cartography and Imaging Sciences Node (IMG), 2015.</p> <p>[3] Cushing, G.E.; Titus, T.N.; Wynne, J.J.; Christensen, P.R. THEMIS observes possible cave skylights on Mars, Geophysical Research Letters, 2007, 34, L17201, doi:10.1029/2007GL030709.</p> <p>[4] Sauro, F.; Pozzobon, R.; Massironi, M.; De Berardinis, P.; Santagata, T.; De Waele, J. Lava tubes on Earth, Moon and Mars: A review on their size and morphology revealed by comparative planetology, Earth-Science Reviews, 2020, Vol. 209, 103288, ISSN 0012-8252, doi.org/10.1016/j.earscirev.2020.103288.</p> <p>[5] Williams, K.E.; McKay, C.P.; Toon, O.B.; Head, J.W. Do ice caves exist on Mars? Icarus, 2010, Vol. 209, Issue 2, pp 358-368, ISSN 0019-1035, doi.org/10.1016/j.icarus.2010.03.039.</p> <p>[6] Wyrick, D.; Ferrill, D.A.; Morris, A.P.; Colton, S.L.; Sims, D.W. Distribution, morphology, and origins of Martian pit crater chains, Journal of Geophysical Research, 2004, 109, E06005, doi:10.1029/2004JE002240.</p>