A Global Perspective on Lunar Granular Flows by V. T. Bickel, S. Loew, J. Aaron, N. Goedhart

“…Here, we build and deploy a convolutional neural network and map 28,101 flow features between 60°N and S by scanning through ∼150,000 Lunar Reconnaissance Orbiter images. We observe that flows are heterogeneously distributed over the Moon, where all major hotspots are located in craters and almost all hotspots are located in the nearside maria. …”
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Lunar Radiometric Measurement Based on Observing China Chang’E-3 Lander with VLBI—First Insight by SongTao Han, ZhongKai Zhang, Jing Sun, JianFeng Cao, Lue Chen, Weitao Lu, WenXiao Li

“…This paper presents the current status and preliminary result of the OCEL and mainly focuses on determination of the lander position, which is about 7 meter in height and 14 meter in plane of lunar surface with respect to LRO (Lunar Reconnaissance Orbiter). Based on accuracy analysis, further optimized OCEL sessions will make use of this target-of-opportunity, the Chang’E-3 lunar lander, as long as it is working. …”
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Application of Machine Learning Techniques to Distinguish between Mare, Cryptomare, and Light Plains in Central Lunar South Pole−Aitken Basin by Frank C. Chuang, Matthew D. Richardson, Jennifer L. Whitten, Daniel P. Moriarty, Deborah L. Domingue

“…We apply machine learning techniques to identify and map resurfacing units in the central South Pole−Aitken (SPA) basin using three Lunar Reconnaissance Orbiter (LRO) mission data sets: 321/415 nm and 566/689 nm band reflectance ratios from Hapke photometrically standardized albedo maps and a Terrain Ruggedness Index map using the Wilson et al. method. …”
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