BOULDERING · A Deep Learning approach for boulder detection –The key to understand planetary surfaces evolution and their crater statistics-based ages

A Deep Learning approach for boulder detection –The key to understand planetary surfaces evolution and their crater statistics-based ages

Boulders are one of the most abundant features on the surfaces of solid planetary bodies. Measuring their size, shape, and orientation can tell us about how they formed as well as help select landing sites that minimize hazard to spacecraft. However, mapping boulders across large areas is a labor-intensive task that often limits the scope and robustness of boulder studies. To overcome this challenge, we trained a machine-learning algorithm to automatically outline boulders on a variety of planetary surfaces using a database of over 30,000 boulders manually mapped from aerial or satellite images of Earth, the Moon, and Mars. Our algorithm, BoulderNet (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JE008013), performs as well as human mappers and outperforms existing automated tools. BoulderNet is made available to the community. Why is it important for society?: The trained boulder algorithm has the potential to assist space agencies and commercial planetary ventures greatly. For example, it can help in the (1) selection of safe landing site, (2) landing of spacecraft in real time, (3) navigation of rovers on planetary surfaces, and (4) mapping of boulders, which could represent an important resource for infrastructure constructions along with other regolith materials. Why is it important for the science community? Boulder mapping could help reveal how planetary surfaces evolved. Craters are very common surface features on many solid planets and moons. During an impact, rock fragments ejected from the crater cavity could be deposited elsewhere on the surface, where they could potentially form secondary craters. Boulders are the only remnants of these ejected materials. Their size and shape, as well as the terrain on which they are found, provide important insight into the ejection mechanisms. In addition, the application of our open-source boulder detection algorithm will be important in the understanding of several geological processes shaping planetary surfaces(e.g., (i) estimate the age of a crater based on its abundance of boulders, since boulders degrade over time; (ii) investigate the magnitude of seismic activity and (iii) map geological units around impact craters. The manuscripts are accompanied by 5 open-access repositories: - Raw drone data of the two fieldworks conducted in the Sierra Nevada (https://zenodo.org/records/14585533) - Raw input and labeled boulder data collected during the project (https://zenodo.org/records/14250970) - Pre-processed images and labels for use with Detectron2 and YOLOv8 (https://zenodo.org/records/14250874) - Code, best trained model setups and weights for YOLOv8 (https://zenodo.org/records/14579518) - Boulder populations around 82 fresh simple impact craters on the Moon and 15 fresh simple impact craters on Mars (https://zenodo.org/records/14253940) And 4 github repositories: - Manipulation of rasters (https://github.com/astroNils/rastertools) - Manipulation of vector data (https://github.com/astroNils/shptools) - Pre-processing, model setup and predictions with Detectron2 Mask R-CNN (https://github.com/astroNils/MLtools) - Pre-processing, model setup and predictions with YOLOv8 (https://github.com/astroNils/YOLOv8-BeyondEarth) More information can be found in the Tech. Report Part B.

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