The safety of lunar landing sites directly impacts the success of lunar exploration missions. This study develops a data-driven predictive model based on machine learning, focusing on engineering safety to assess the suitability of lunar landing sites and provide insights into key factors and feature representations. Six critical engineering factors were selected as constraints for evaluation: slope, elevation, roughness, hillshade, optical maturity, and rock abundance. The XGBoost model was employed to simulate and predict the characteristics of landing areas and Bayesian optimization was used to fine-tune the model’s key hyperparameters, enhancing its predictive performance. The results demonstrate that this method effectively extracts relevant features from multi-source remote sensing data and quantifies the suitability of landing zones, achieving an accuracy of 96% in identifying landing sites (at a resolution of 0.1° × 0.1°), with AUC values exceeding 95%. Notably, slope was recognized as the most critical factor affecting safety. Compared to assessment processes based on Convolutional Neural Networks (CNNs) and Random Forest (RF) models, XGBoost showed superior performance in handling missing values and evaluating feature importance accuracy. The findings suggest that the BO-XGBoost model shows notable classification performance in evaluating the suitability of lunar landing sites, which may provide valuable support for future landing missions and contribute to optimizing lunar exploration efforts.

The external environment in the transitional zone of the ecological barrier is fragile, and economic growth has resulted in a series of land degradation issues, significantly impacting regional economic development and the ecological environment. Therefore, monitoring, assessing, and predicting land use changes are crucial for ecological security and sustainable development. This study developed an integrated model comprising convolutional neural network, cellular automata, and Markov chain to forecast the land use status of western Jilin, located in the transitional zone of the ecological barrier, by the year 2030. Additionally, the study evaluated the role of land use policies in the context of land use changes in western Jilin. The findings demonstrate that the coupled modeling approach exhibits excellent predictive performance for land use prediction in western Jilin, yielding a Kappa coefficient of 93.26%. Policy drivers play a significant role in shaping land use patterns in western Jilin, as evidenced by the declining farmland accompanied by improved land utilization, the sustained high levels of forest aligning with sustainable development strategies, the ongoing restoration of waters and grassland, which are expected to show positive growth by 2030, and the steady growth in built-up areas. This study contributes to understanding the dynamics of land use in the transitional zone of the ecological barrier, thereby promoting sustainable development and ecological resilience in the region.

As mineral resources become increasingly scarce, the search for potential ore deposits presents a significant challenge in geological exploration. Subjective factors often constrain traditional knowledge-driven approaches, while purely data-driven methods may overlook the geological significance of data relationships, potentially compromising the accuracy and reliability of predictions. To address these issues, this study proposes a novel mineral prediction model that integrates fuzzy comprehensive evaluation with both knowledge-driven and data-driven approaches (FCEKDD), resulting in an optimized comprehensive mineral prediction model based on multi-source geoscience data. This model establishes comprehensive exploration indicators based on expert knowledge and quantitatively assesses these indicators through fuzzy evaluation methods to evaluate mineralization potential, thereby delineating exploration targets within the study area. Ultimately, a three-layer predictive framework is constructed using the C-A fractal method. Experimental results indicate that 57.1% of known mineral points are within the primary prediction zone, thus demonstrating the model’s high predictive accuracy. Comparisons with a random forest model reveal that the FCEKDD model has advantages in addressing geoscience data uncertainty and interpreting geological phenomena. This research validates the reliability and applicability of the proposed model in mineral exploration mapping, providing an effective solution for future mineral resource exploration.

Land use change monitoring is a common theme in achieving sustainable development, while research on ecological barrier transition zones is relatively scarce. This study quantitatively analyzes the characteristics and patterns of land use change in Western Jilin, located in the transitional zone between the northeast forest belt and the northern sand prevention belt, from 1990 to 2020. Land dynamic change index and transition matrix are used to quantify the rates and intensities, and conversions between different land use types over time, respectively. Geodetector is adopted to analyze the impact of 12 factors on 12 types of land use change, such as using the factor detector to quantify the influence of temperature on the conversion from cropland to unused land. The results indicate that from 1990 to 2020, there have been noticeable changes in the area of various land use types in western Jilin. However, the conversion types are relatively limited, mainly involving interchanges between cropland, grassland, unused land, and water bodies. The cropland has increased by 20% overall, but 16% of that increase occurred from 1990–2000. The woodland area has steadily increased at a growth rate of 5–8% from 2000–2020, aligning with sustainable development strategies. Water bodies and grasslands are undergoing continuous recovery, and a positive growth trend is predicted to emerge by 2030. The built-up land is steadily expanding. The influencing factors vary for different types of land-use change. In a short time, policy factors play a significant role in land use, such as the implementation of the “River-lake Connection Project”, which has helped to reduce water-body fragmentation and enabled the stable recovery of water resources. However, in the long term, multiple topographic, climatic, and anthropogenic factors exhibit interactive effects in the land use change process in the area. Governments can take corresponding measures and management policies based on the influence of these factors to allocate and plan land use rationally.

The permanently shadowed regions of the lunar South Pole have become a key target for international lunar exploration due to their unique scientific value and engineering challenges. In order to effectively screen suitable landing zones near the lunar South Pole, this research proposes a comprehensive evaluation method based on a self-organizing map (SOM). Using multi-source remote sensing data, the method classifies and analyzes candidate landing zones by combining scientific purposes (such as hydrogen abundance, iron oxide abundance, gravity anomalies, water ice distance analysis, and geological features) and engineering constraints (such as Sun visibility, Earth visibility, slope, and roughness). Through automatic clustering, the SOM model finds the important regions. Subsequently, it integrates with a supervised learning model, a random forest, to determine the feature importance weights in more detail. The results from the research indicate the following: the areas suitable for landing account for 9.05%, 5.95%, and 5.08% in the engineering, scientific, and synthesized perspectives, respectively. In the weighting analysis of the comprehensive data, the weights of Earth visibility, hydrogen abundance, kilometer-scale roughness, and slope data all account for more than 10%, and these are thought to be the four most important factors in the automated site selection process. Furthermore, the kilometer-scale roughness data are more important in the comprehensive weighting, which is in line with the finding that the kilometer-scale roughness data represent both surface roughness from an engineering perspective and bedrock geology from a scientific one. In this study, a local examination of typical impact craters is performed, and it is confirmed that all 10 possible landing sites suggested by earlier authors are within the appropriate landing range. The findings demonstrate that the SOM-model-based analysis approach can successfully assess lunar South Pole landing areas while taking multiple constraints into account, uncovering spatial distribution features of the region, and offering a rationale for choosing desired landing locations.