Chang’e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of the Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. In particular, the crater size–frequency distribution information of the landing area is critical to understanding the provenance of the CE-6 lunar samples to be returned and can be used to verify and refine the lunar chronology model by combining with the radioisotope ages of the relevant samples. In this research, a digital orthophoto map (DOM) mosaic with resolution of 3 m/pixel of the CE-6 landing area was generated from the 743 Narrow Angle Camera of the Lunar Reconnaissance Orbiter Camera. Based on the DOM, craters were extracted by an automated method and checked manually. A total of 770,731 craters were extracted in the whole area of 246 km × 135 km, 511,484 craters of which were within the mare area. Systematic analyses of the crater distribution, completeness, spatial density, and depth-to-diameter ratio were conducted. Geologic model age estimation was carried out in the mare area that was divided into three geologic units according to the TiO₂ abundance. The result showed that the east part of the mare had the oldest model age of $\mu {3.27}_{-0.045}^{+0.036}$ Ga, and the middle part of the mare had the youngest model age of $\mu {2.49}_{-0.073}^{+0.072}$ Ga. The crater catalogue and the surface model age analysis results were used to support topographic and geologic analyses of the pre-selected landing area of the CE-6 mission before the launch and will contribute to further scientific researches after the lunar samples are returned to Earth.

The Chang’e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole–Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed. The model first incorporated a Partial Self-Attention (PSA) mechanism at the end of the Backbone, allowing the model to enhance global perception and reduce missed detections with a low computational cost. Then, a Gather-and-Distribute mechanism (GD) was integrated into the Neck, enabling the model to fully fuse multi-level feature information and capture global information, enhancing the model’s ability to detect impact craters of various sizes. The experimental results showed that the YOLOv8-LCNET model performs well in the impact crater detection task, achieving 87.7% Precision, 84.3% Recall, and 92% AP, which were 24.7%, 32.7%, and 37.3% higher than the original YOLOv8 model. The improved YOLOv8 model was then used for automatic crater detection in the CE-6 landing area (246 km × 135 km, with a DOM resolution of 3 m/pixel), resulting in a total of 770,671 craters, ranging from 13 m to 19,882 m in diameter. The analysis of this impact crater catalogue has provided critical support for landing site selection and characterization of the CE-6 mission and lays the foundation for future lunar geological studies.

Lunar digital elevation models (DEMs) are a fundamental data source for lunar research and exploration. However, high-resolution DEM products for the Moon are only available in some local areas, which makes it difficult to meet the needs of scientific research and missions. To this end, we have previously developed a deep learning-based method (LDEMGAN1.0) for single-image lunar DEM reconstruction. To address issues such as loss of detail in LDEMGAN1.0, this study leverages the inherent structural self-similarity of different DEM data from the same lunar terrain and proposes an improved version, named LDEMGAN2.0. During the training process, the model computes the self-similarity graph (SSG) between the outputs of the LDEMGAN2.0 generator and the ground truth, and incorporates the self-similarity loss (SSL) constraint into the network generator loss to guide DEM reconstruction. This improves the network’s capacity to capture both local and global terrain structures. Using the LROC NAC DTM product (2 m/pixel) as the ground truth, experiments were conducted in the Apollo 11 landing area. The proposed LDEMGAN2.0 achieved mean absolute error (MAE) of 1.49 m, root mean square error (RMSE) of 2.01 m, and structural similarity index measure (SSIM) of 0.86, which is 46.0%, 33.4%, and 11.6% higher than that of LDEMGAN1.0. Both qualitative and quantitative evaluations demonstrate that LDEMGAN2.0 enhances detail recovery and reduces reconstruction artifacts.

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