  Boulders form from a variety of geological processes, which their size,
shape, and orientation may help us better understand. Furthermore, they
represent potential hazards to spacecraft landing that need to be
characterized. However, mapping individual boulders across vast areas is
extremely labor-intensive, often limiting the extent over which they are
characterized and the statistical robustness of obtained boulder morphometrics.
To automate boulder characterization, we use an instance segmentation neural
network, Mask R-CNN, to detect and outline boulders in high-resolution
satellite images. Our neural network, BoulderNet, was trained from a dataset of
> 33,000 boulders in > 750 image tiles from Earth, the Moon, and Mars.
BoulderNet not only correctly detects the majority of boulders in images, but
it identifies the outline of boulders with high fidelity, achieving average
precision and recall values of 72% and 64% relative to manually digitized
boulders from the test dataset, when only detections with
intersection-over-union ratios > 50% are considered valid. These values are
similar to those obtained by human mappers. On Earth, equivalent boulder
diameters, aspect ratios, and orientations extracted from predictions were
benchmarked against ground measurements and yield values within 15%, 0.20, and
20 degrees of their ground-truth values, respectively. BoulderNet achieves
better boulder detection and characterization performance relative to existing
methods, providing a versatile open-source tool to characterize entire boulder
fields on planetary surfaces.
