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.

通过使用一个实例分割神经网络（BoulderNet）并利用高分辨率卫星图像，以不同行星表面为数据集，我们可以自动化检测和轮廓描绘巨石，达到与人工描绘相似的性能，并提供了一个通用的开源工具来描述整个巨石领域。

高分辨率卫星图像上行星表面巨石的自动特征描述

Automatic characterization of boulders on planetary surfaces from  high-resolution satellite images

Craters are one of the most prominent features on planetary surfaces, used in
applications such as age estimation, hazard detection, and spacecraft
navigation. Crater detection is a challenging problem due to various aspects,
including complex crater characteristics such as varying sizes and shapes, data
resolution, and planetary data types. Similar to other computer vision tasks,
deep learning-based approaches have significantly impacted research on crater
detection in recent years. This survey aims to assist researchers in this field
by examining the development of deep learning-based crater detection algorithms
(CDAs). The review includes over 140 research works covering diverse crater
detection approaches, including planetary data, craters database, and
evaluation metrics. To be specific, we discuss the challenges in crater
detection due to the complex properties of the craters and survey the DL-based
CDAs by categorizing them into three parts: (a) semantic segmentation-based,
(b) object detection-based, and (c) classification-based. Additionally, we have
conducted training and testing of all the semantic segmentation-based CDAs on a
common dataset to evaluate the effectiveness of each architecture for crater
detection and its potential applications. Finally, we have provided
recommendations for potential future works.

该研究综述了基于深度学习的撞击坑检测算法的发展，讨论了撞击坑检测的挑战以及不同的算法类别，包括语义分割、目标检测和分类。研究还对在常见数据集上进行的语义分割算法的训练和测试进行了评估，并提出了未来研究的建议。