Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach
The identification of optimal landing sites is a critical first step for successful missions to the Moon and other extraterrestrial bodies, necessitating the integration of various environmental factors over large spatial scales. At the lunar south pole, site selection must balance engineering safet...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10542190/ |
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| author | Yongjiu Feng Haoteng Li Xiaohua Tong Pengshuo Li Rong Wang Shurui Chen Mengrong Xi Jingbo Sun Yuhao Wang Huaiyu He Chao Wang Xiong Xu Huan Xie Yanmin Jin Sicong Liu |
| author_facet | Yongjiu Feng Haoteng Li Xiaohua Tong Pengshuo Li Rong Wang Shurui Chen Mengrong Xi Jingbo Sun Yuhao Wang Huaiyu He Chao Wang Xiong Xu Huan Xie Yanmin Jin Sicong Liu |
| author_sort | Yongjiu Feng |
| collection | DOAJ |
| description | The identification of optimal landing sites is a critical first step for successful missions to the Moon and other extraterrestrial bodies, necessitating the integration of various environmental factors over large spatial scales. At the lunar south pole, site selection must balance engineering safety with areas of high scientific interest, requiring extensive analysis of potential locations. Although intelligent algorithms have been increasingly investigated for this purpose, the application of deep learning techniques in landing site selection remains unexplored. In this study, we employ one-dimensional convolutional neural networks (1D-CNNs) to quantitatively assess potential landing sites for exploration and lunar base construction, considering both scientific and engineering criteria. We also evaluate the influence of various factors on site selection using Shapley additive explanations (SHAP) values. The 1D-CNN model demonstrates robust performance across training, validation, and testing phases. Potential landing sites identified comprise less than 1% of the total study area, with factors such as visibility, volatile distribution, topography, and geological characteristics playing crucial roles. By applying operational constraints, we delineate sites suitable for direct landings and further refine this subset for base construction based on stringent requirements for resource utilization and energy sustainability. The combined use of CNN and SHAP enables more effective potential site screening and a deeper understanding of the factors influencing selection. Our findings offer a valuable framework for future lunar south pole expeditions, potentially minimizing manual survey efforts and enhancing the precision of landing site selection. |
| format | Article |
| id | doaj-art-9bc9010d18b64b16ab91fdaca4b29d08 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-9bc9010d18b64b16ab91fdaca4b29d082025-08-20T02:52:59ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117109981101510.1109/JSTARS.2024.340707010542190Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network ApproachYongjiu Feng0https://orcid.org/0000-0001-8772-7218Haoteng Li1https://orcid.org/0009-0001-5121-7205Xiaohua Tong2https://orcid.org/0000-0002-1045-3797Pengshuo Li3Rong Wang4Shurui Chen5https://orcid.org/0000-0002-9891-0672Mengrong Xi6Jingbo Sun7Yuhao Wang8Huaiyu He9Chao Wang10https://orcid.org/0000-0001-7565-2124Xiong Xu11https://orcid.org/0000-0003-3510-4160Huan Xie12https://orcid.org/0000-0003-3272-7848Yanmin Jin13Sicong Liu14https://orcid.org/0000-0003-1612-4844College of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaInstitute of Geology and Geophysics, Chinese Academy of Science, Beijing, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, and the Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, ChinaThe identification of optimal landing sites is a critical first step for successful missions to the Moon and other extraterrestrial bodies, necessitating the integration of various environmental factors over large spatial scales. At the lunar south pole, site selection must balance engineering safety with areas of high scientific interest, requiring extensive analysis of potential locations. Although intelligent algorithms have been increasingly investigated for this purpose, the application of deep learning techniques in landing site selection remains unexplored. In this study, we employ one-dimensional convolutional neural networks (1D-CNNs) to quantitatively assess potential landing sites for exploration and lunar base construction, considering both scientific and engineering criteria. We also evaluate the influence of various factors on site selection using Shapley additive explanations (SHAP) values. The 1D-CNN model demonstrates robust performance across training, validation, and testing phases. Potential landing sites identified comprise less than 1% of the total study area, with factors such as visibility, volatile distribution, topography, and geological characteristics playing crucial roles. By applying operational constraints, we delineate sites suitable for direct landings and further refine this subset for base construction based on stringent requirements for resource utilization and energy sustainability. The combined use of CNN and SHAP enables more effective potential site screening and a deeper understanding of the factors influencing selection. Our findings offer a valuable framework for future lunar south pole expeditions, potentially minimizing manual survey efforts and enhancing the precision of landing site selection.https://ieeexplore.ieee.org/document/10542190/1D-CNNfactor importanceinternational lunar research station (ILRS)landing site selectionLunar south polewater-ice |
| spellingShingle | Yongjiu Feng Haoteng Li Xiaohua Tong Pengshuo Li Rong Wang Shurui Chen Mengrong Xi Jingbo Sun Yuhao Wang Huaiyu He Chao Wang Xiong Xu Huan Xie Yanmin Jin Sicong Liu Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1D-CNN factor importance international lunar research station (ILRS) landing site selection Lunar south pole water-ice |
| title | Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach |
| title_full | Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach |
| title_fullStr | Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach |
| title_full_unstemmed | Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach |
| title_short | Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach |
| title_sort | optimized landing site selection at the lunar south pole a convolutional neural network approach |
| topic | 1D-CNN factor importance international lunar research station (ILRS) landing site selection Lunar south pole water-ice |
| url | https://ieeexplore.ieee.org/document/10542190/ |
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