The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes.

It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area.

The investigation of lunar polar regions is critical for understanding the characteristics of the moon, such as the crustal evolution, volatile retention, and in-situ resource utilization. In addition, it serves as an important reference and prerequisite for future lunar exploration missions and the establishment of a long-term scientific research station. However, quantitative multi-oxide mapping in these regions remains limited since previous efforts mainly focused on FeO at coarse spatial resolutions (~1 km/pixel). To address this gap, an inversion model for a one-dimensional convolutional neural network (1D-CNN) to obtain high-resolution (~500 m/pixel) abundance distributions of six major oxides—FeO, TiO₂, Al₂O₃, CaO, MgO, and SiO₂—across the lunar polar regions (65°–90°N/S) is investigated in this research. The developed 1D-CNN model, utilizing the KAGUYA SP hyperspectral data and oxide results from Bian et al. (2025) as sample data, achieved high inversion accuracy, as evidenced by R² > 0.94 for all oxides and RMSE < 0.3 wt.% except for Al₂O₃. The inverted oxide maps reveal that the materials in the south polar region (83°–90°S) are compositionally uniform and dominated by feldspathic highland lithologies, which are distinct from the ejecta or mantle-derived materials typically associated with the South Pole–Aitken basin. These results provide a new insight into the crustal origin of the lunar south pole. Moreover, comparisons of the nine Artemis Ⅲ candidate landing regions showed overall compositional homogeneity, suggesting similar highland-derived sources, and of the nine regions, the Slater Plain and de Gerlache Rim 2 areas were identified as optimal targets for capturing resource diversity within feasible operational ranges. These findings not only fill a critical gap in quantitative geochemical mapping of the lunar poles but also provide essential references for the Artemis Ⅲ and Chang’E-7 missions, supporting landing region selection for future missions, resource evaluation, and in-situ utilization strategies.