#5914. Analyzing multi–domain learning for enhanced rockfall mapping in known and unknown planetary domains

August 2026publication date
Proposal available till 03-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Engineering (miscellaneous);
Computer Science Applications;
Computers in Earth Sciences;
Atomic and Molecular Physics, and Optics;
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Abstract:
Rockfalls are small–scale mass wasting events that have been observed across the solar system. They provide valuable information about the endo- and exogenic activity of their host body, but are difficult to identify and map in satellite imagery, especially on global scales and in big data sets. Past work implemented convolutional neural networks to automate rockfall mapping on the Moon and Mars with the caveat of (1) achieving sub–optimal performance and (2) requiring substantial manual image labeling efforts. Mixing annotated image data from the Moon and Mars while keeping the total number of labels constant, we show that including a small number (10%) of rockfall labels from a foreign domain (e.g. Moon) during detector training can increase performance in the home domain (e.g. Mars) by up to 6% Average Precision (AP) in comparison to a purely home domain-trained detector.
Keywords:
Ceres; Domain adaptation; Mars; Moon; Rockfall; Transfer learning

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