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Chinese Academy of Sciences Institutional Repositories Grid
ResGAT-F: a novel graph neural network-based approach for evaluating landing suitability in the lunar southern polar region

文献类型:期刊论文

作者Shibo Wen; Yongzhi Wang; Xingyu Chen; Qizhou Gong; Jianzhong Liu; Xiaoxi Kang; Hengxi Liu; Kai Zhu; Sheng Zhang
刊名International Journal of Digital Earth
出版日期2025
卷号18期号:1
关键词Lunar South Pole landing Suitability graph Neural Network sensemble Learning water Ice Detection
DOI10.1080/17538947.2025.2547291
英文摘要

Landing suitability evaluation in the Lunar Southern Polar region is critical for future exploration, it requires integrating various environmental factors to balance safety and scientific value. This study proposes a Residual Connection Graph Attention Forest (ResGAT-F) model, which systematically integrates multi-source spatial data to extract regional features and environmental relationships, enabling a quantitative assessment of landing suitability that addressing safety and multi-disciplinary scientific exploration scenarios. Results show all ResGAT sub-models achieve over 96% accuracy based on pixel-wise labels generated by the adapted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) from multiple small-sample regions. Ensemble ResGAT-F attains AUC above 0.92, outperforming the baseline model Attn-CNN (accuracy: 93%, AUC: 0.86). A 256 m resolution suitability map between 80°S to 90°S was generated and been scored (scoring ≥8 means highly suitable), which can evaluate site suitability across the region. Only 7.81% of the area meets safety requirements and potential of multi-target scientific exploration. Suitability evaluations for Artemis III candidate landing zones, such as Malapert Massif, indicates 28% of this area meets the requirements. The ResGAT-F in handling complex, multi-dimensional lunar data shows potential for supporting future landing missions and improving lunar exploration planning.

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语种英语
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专题地球化学研究所_月球与行星科学研究中心
天体地球化学研究组
作者单位1.College of Geoexploration Science and Technology, Jilin University, Changchun, People’s Republic of China
2.Institute of Integrated Information for Mineral Resources Prediction, Jilin University, Changchun, People’s Republic of China
3.College of Instrumentation and Electrical Engineering, Jilin University, Changchun, People’s Republic of China
4.Center for Lunar and Planetary Science, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, People’s Republic of China
5.CAS Center for Excellence in Comparative Planetology, Chinese Academy of Sciences, Hefei, People’s Republic of China
6.Deep Space Exploration Laboratory, Beijing, People’s Republic of China
7.Lunar Exploration and Space Engineering Centre, China National Space Administration, Beijing, People’s Republic of China
8.College of Resources and Environmental Engineering, Guizhou University, Guiyang, People’s Republic of China
推荐引用方式
GB/T 7714
Shibo Wen,Yongzhi Wang,Xingyu Chen,et al. ResGAT-F: a novel graph neural network-based approach for evaluating landing suitability in the lunar southern polar region[J]. International Journal of Digital Earth,2025,18(1).
APA Shibo Wen.,Yongzhi Wang.,Xingyu Chen.,Qizhou Gong.,Jianzhong Liu.,...&Sheng Zhang.(2025).ResGAT-F: a novel graph neural network-based approach for evaluating landing suitability in the lunar southern polar region.International Journal of Digital Earth,18(1).
MLA Shibo Wen,et al."ResGAT-F: a novel graph neural network-based approach for evaluating landing suitability in the lunar southern polar region".International Journal of Digital Earth 18.1(2025).

入库方式: OAI收割

来源:地球化学研究所

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