Noble gases in comet 67P/Churyumov-Gerasimenko indicate nonhomogenized protoplanetary disk material at its location of formation.

Past exploration missions have revealed that the lunar topography is eroded through mass wasting processes such as rockfalls and other types of landslides, similar to Earth. We have analyzed an archive of more than 2 million high-resolution images using an AI and big data-driven approach and created the first global map of 136.610 lunar rockfall events. Using this map, we show that mass wasting is primarily driven by impacts and impact-induced fracture networks. We further identify a large number of currently unknown rockfall clusters, potentially revealing regions of recent seismic activity. Our observations show that the oldest, pre-Nectarian topography still hosts rockfalls, indicating that its erosion has been active throughout the late Copernican age and likely continues today. Our findings have important implications for the estimation of the Moon’s erosional state and other airless bodies as well as for the understanding of the topographic evolution of planetary surfaces in general.

The analysis of rockfall distribution and magnitude is a useful tool to study the past and current endogenic and exogenic activity of Mars. At the same time, tracks left by rockfalls provide insights into the mechanical properties of the martian surface. While a wealth of high-resolution space-borne image data are available, manual mapping of displaced boulders with tracks is inefficient and slow, resulting in 1) a small total number of mapped features, 2) inadequate statistics, and 3) a sub-optimal utilization of the available big data. This study implements a deep learning-driven approach to automatically detect and map martian boulders with tracks in High Resolution Imaging Science Experiment (HiRISE) imagery. Six off-the-shelf neural networks have been trained either on martian or lunar rockfall data, or a combination of both, and are able to achieve a maximum recall of up to 0.78 and a maximum precision of up to 1.0, with a mean average precision of 0.71. The fusion of training data from different planets and sensors results in an increased detection precision, highlighting the value of domain generalization and multi-domain learning. Average processing time per HiRISE image is ~45 s using an NVIDIA Titan Xp, which is more than one order of magnitude faster than a human operator. The developed deep learning-driven infrastructure can be deployed to map martian rockfalls on a global scale and within a realistic timeframe.

Consideration is given to the estimates for the strength of the consolidated material forming the bulk of the nucleus of comet 67P Churyumov–Gerasimenko and those for the strength of the surface material overlying the consolidated material at the sites of the first and last contact of the lander with the nucleus. The strength of the consolidated material was estimated by analyzing the terrain characteristics of the steep cliffs, where the material is exposed on the surface. Based on these estimates, the tensile strength of the material is in the range from 1.5 to 100 Pa; the shear strength, from ∼13 to ⩾30 Pa; and the compressive strength, from 30 to 150 Pa, possibly up to 1.5 kPa. These are very low strength values. Given the dependence of the measurement results on the size of the measured object, they are similar to those of fresh dry snow at –10°C. The (compressive) strength of the surface material at the site of the first touchdown of on the nucleus is estimated from the measurements of the dynamics of the surface impact by the spacecraft’s legs and the geometry of the impact pits as 1–3 kPa. For comparison with the measurement results for ice-containing materials in terrestrial laboratories, it needs to be taken into account that the rate of deformation by ’s legs is four orders of magnitude higher than that in typical terrestrial measurements, leading to a possible overestimation of the strength by roughly an order of magnitude. There was an attemp to put one of the MUPUS sensors into the surface material at the site of the last contact of with the nucleus. Noticeable penetration of the tester probe was not achieved that led to estimation of the minimum compressive strength of the material to be ⩾4 MPa4 This fairly high strength appears to indicate the presence of highly porous ice with grains “frozen” at contacts.