A Labeled Image Dataset for Deep Learning-Driven Rockfall Detection on the Moon and Mars

Background: The term rockfall describes the rapid displacement of a large, usually meter-sized block of rock down-slope, triggered by, for example, endogenic or exogenic events like impacts, quakes or rainfall. In a remote sensing context, the term rockfall is also being used to describe the characteristic geomorphic deposit of a rockfall event that can be identified from an air- or space-borne perspective, i.e., the combination of a displaced boulder and the track it carved into the slope substrate while bouncing, rolling, and sliding over the surface (also called `boulder with track' or `rolling boulder'). In planetary science, the spatial distribution and frequency of rockfalls provide insights into the global erosional state and activity of a planetary body while their tracks act as tools that allow for the remote estimation of the surface strength properties of yet unexplored regions in preparation of future ground exploration missions, such as the lunar pyroclastic, polar sunlit and permanently shadowed regions of the Moon. Due to their small physical size (meters), the identification and mapping of rockfalls in planetary satellite imagery is challenging and very time-consuming, however. For this reason, Bickel et al. (2018) and Bickel et al. (2020) trained convolutional neural networks to automate rockfall mapping in lunar and martian satellite imagery. Parts of the unpublished datasets used for earlier work have now been complemented with newly labeled data to create a well-balanced dataset of 2,822 lunar and martian rockfall labels (which we call `RMaM-2020' --- [R]ockfall [Ma]rs [M]oon [2020], 416 MB in total, available here) that can be used for deep learning and other data science applications. Here, balanced means that the labels have been derived from imagery with a wide and continuous range of properties like spatial resolution, solar illumination, and others. So far, this dataset has been used to analyze the benefits of multi-domain learning on rockfall detector performance (Mars & Moon vs. Moon-only or Mars-only), but there are numerous other (non-planetary science) applications such as for featurization, feature or target recognition (aircraft/spacecraft autonomy), and data augmentation experiments.

背景：岩石崩塌一词，通常指由内源或外源事件，如撞击、地震或降雨等引发的，通常尺寸为米级的大块岩石沿坡面快速位移的现象。在遥感领域，岩石崩塌一词亦被用于描述岩石崩塌事件的特征地貌沉积，该沉积物可通过航空或航天视角识别，即一块被位移的巨石及其在弹跳、滚动和滑行过程中在坡面基岩上刻画的轨迹（亦称为‘带有轨迹的巨石’或‘滚动巨石’）。在行星科学领域，岩石崩塌的空间分布和频率可为行星体的全球侵蚀状态和活动提供洞察，而其轨迹则作为工具，允许对尚未探索地区的地表强度特性进行远程估算，为未来的地面探测任务做好准备，例如月球上的火山喷发、极地阳光照射区域和永久阴影区域。然而，由于岩石崩塌的物理尺寸较小（米级），在行星卫星图像中对岩石崩塌的识别和制图是一项极具挑战性和耗时的工作。鉴于此，Bickel 等人（2018 年）和Bickel 等人（2020 年）训练了卷积神经网络来自动化月球和火星卫星图像中的岩石崩塌制图。为补充先前工作中未公开发布的数据集，现已新增标注数据，构建了一个包含 2,822 项月球和火星岩石崩塌标签的平衡数据集（我们称之为‘RMaM-2020’——[R]ockfall [Ma]rs [M]oon [2020]，总容量 416 MB，可在此处获取），该数据集可用于深度学习和其他数据科学应用。在此，‘平衡’意味着这些标签是从具有广泛且连续特性的图像中提取的，如空间分辨率、太阳光照等。迄今为止，该数据集已用于分析多领域学习对岩石崩塌检测器性能的益处（火星与月球对比仅月球或仅火星），但还有众多其他（非行星科学）应用，例如特征化、特征或目标识别（飞机/航天器自主性）以及数据增强实验。