ResGAT-F: a novel graph neural network-based approach for evaluating landing suitability in the lunar southern polar region
Year: 2025
Type: article
Abstract: 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.
Authors: Shibo Wen, Yongzhi Wang, Xingyu Chen, Qizhou Gong, Jianzhong Liu, Xiaoxi Kang, Hengxi Liu, Kai Zhu, Sheng Zhang
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Award: G3609930293
OpenAlex ID: W4413313154