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We demonstrate the creation of a large area of high-resolution (260 × 209 km2 at 1 m/pixel) DTM mosaic from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images over the Chang'E-4 landing site at Von Kármán crater using an in-house deep learning-based 3D modelling system developed at University College London, called MADNet, trained with lunar orthorectified images and digital terrain models (DTMs). The resultant 1 m DTM mosaic is co-aligned with the Chang'E-2 (CE-2) and the Lunar Orbiter Laser Altimeter (LOLA)—SELenological and Engineering Explorer (SELENE) blended DTM product (SLDEM), providing high spatial and vertical congruence. In this paper, technical details are briefly discussed, along with visual and quantitative assessments of the resultant DTM mosaic product. The LROC NAC MADNet DTM mosaic was compared with three independent DTM datasets, and the mean differences and standard deviations are as follows: PDS photogrammetric DTM at 5 m grid-spacing had a mean difference of −0.019 ± 1.09 m, CE-2 DTM at 20 m had a mean difference of −0.048 ± 1.791 m, and SLDEM at 69 m had a mean difference of 0.577 ± 94.940 m. The resultant LROC NAC MADNet DTM mosaic, alongside a blended LROC NAC and CE-2 MADNet DTM mosaic and a separate LROC NAC, orthorectified image mosaic, are made publicly available via the ESA planetary science archive's guest storage facility. [ABSTRACT FROM AUTHOR]

The Chang’E-2 digital elevation model (DEM, named CE2TMap2015), which is the highest resolution DEM product in China, was released with global coverage of the lunar surface. A detailed geometric quality assessment of the product is desirable for wider applications by users. A novel procedure for evaluating DEM geometric quality, which investigates both the global geometric uncertainty and local gross errors, is proposed in this paper. First, the DEM was divided into regular blocks and matched with the reference DEM. Then, the characteristics of errors were investigated using the statistical information of the matched tie points. Next, the local outlier factor (LOF) algorithm was performed to locate the gross errors. In our experiment, CE2TMap2015 was evaluated using the proposed method, with SLDEM2015 as the reference DEM. The results show that there were widespread geometric inconsistencies with an area-weighted average of 183.1 m horizontally (with the standard deviation being 101.2 m) and 2.3 m vertically (with the standard deviation being 15.4 m). Gross errors were detected automatically and were excluded in the statistical analysis. The periodic errors were extracted in the frequency domain using a Fourier transform. Our research results provide instructional information for the utilization of CE2TMap2015 by world-wide users. The proposed method can be used in the assessment of other planetary DEMs.