Background: Rural settlements are undergoing significant changes under the rapid urbanisation, and understanding their evolution characteristics and surrounding land use will provide a basis for land spatial planning. This study takes Pingnan County, Fujian Province, China as study area, reveals the characteristics of spatial–temporal evolution and surrounding land use transition of settlements during 1985–2020 through landscape metrics, spatial “hot spot” analysis, scale classification statistics, rank-size model, Gini index, land use transition matrix. Results: The results show that: (1) Concerning the size and morphological characteristics, the settlements have witnessed a considerable increase in number and scale while remaining stable in shape. (2) Regarding spatial distribution characteristics, the settlements became more evenly spread, forming three main hotspot clusters. (3) Concerning scale structure characteristics, there are significant differences in scale, growth rates, and polarisation of settlements; the polarisation of large settlements shifted from a marked divergence before 2010 to a more balanced trend after 2010. (4) The land use transition around settlements differed in buffer zones and periods. During 1985–2010, settlement expansion heavily depended on cropland, depleting nearby resources, with an increase of woodland and grassland. During 2010–2020, expansion integrated cropland, woodland, and grassland, with cropland growth mainly encroaching on woodland and grassland. Conclusions: The study's findings are significant for optimising rural settlement structure in mountains and promoting sustainable land resource use.

Amid global industrialization and urbanization, mountainous rural settlements, especially those in metropolitan fringe area, are experiencing significant spatial changes in location and scale. This study takes Pingnan County, Fujian Province, China, as an example. Utilizing land use data and employing methods including standard deviation ellipse, average nearest neighbor index, kernel density estimation, spatial hotspot detection, binary logistic regression model, and Geodetector, this study aims to scientifically identify the spatial pattern characteristics and influencing factors of its settlements. The results show that: (1) The spatial distribution of settlements in Pingnan County is biased toward the southern part of the county; the center of settlement's spatial distribution is located south of the junction of Gufeng Town and Pingcheng Town; the spatial distribution trend of settlements is north-east-southwest. Settlements are generally aggregated, and the aggregation degree of Gufeng Town is obviously lower than that of other towns. (2) The density distribution of settlements presents a “core-periphery” structure and a “north-south linear” structure in space; the spatial pattern characteristics show high-density, large patches in Gufeng Town, high-density, small patches in Changqiao Town, Tangkou Town and Gantang Town, and medium-density or low-density, small patches in other towns. (3) Settlement location is mainly affected by the elevation, distance to cultivated land, and distance to main roads, while settlement scale is mainly affected by slope, relief degree of land surface, and distance to urban centers. The interaction between these factors exhibits enhancement effects, with natural terrain and location conditions exerting the most prominent influence. These findings underscore the strong constraints posed by natural topography on mountainous rural settlements in metropolitan fringe areas, coupled with a more pronounced influence from socio-economic factors. The study's results hold significant implications for optimizing the layout of such settlements, guiding land spatial planning, and promoting rural revitalization.

On-water sightseeing plays a key role in the tourism of traditional Chinese landscapes. The on-water landscape affects tourism potential and the quality of urban landscapes. Current research on river landscape is mainly based on remote sensing images or on-land approaches, while studies of on-water perspective landscapes at different river scales is lacking. In this paper, with Guilin city rivers taken as an example, we adopt image semantic segmentation technology to evaluate the visual landscape characteristics under different river scales, and subsequently employ automatic linear modeling to screen important factors affecting aesthetic quality. The results reveal the obvious differences between the on-water landscape characteristics of different scale rivers. The on-water landscape quality of large- and small-scale rivers is mainly affected by seven and four factors, respectively. The Karst landform of Guilin is observed to significantly improve the on-water landscape quality of large-scale rivers. By considering the impact mechanism of landscape composition on the aesthetic quality and the different scale rivers, we propose several aesthetic quality improvement strategies based on low-cost methods, including the planting of vegetation and the micro renewal of artificial constructions. This study contributes to the intelligent evaluation of urban on-water landscape and provides reference for on-water route selection and urban planning.

In rapidly urbanizing areas, there is a notable aggregation of vitality in high-density urban environments, accompanied by an increasing discrepancy between the supply and demand of urban green space (UGS). This study presented an integrated framework comprising a model for UGS supply-demand coupling coordination and a measure of urban vitality. Using downtown Beijing as a case study, the Gini coefficient assessed UGS supply-demand disparities across different vitality types. The study examined how UGS supply and demand factors interact with urban vitality, revealing the impact of UGS supply-demand imbalances on various dimensions of vitality and the UGS mismatches experienced by different vitality groups. The study showed that: (1) 63.29% of central Beijing’s areas had low UGS supply-demand coordination, with 39.23% experiencing UGS mismatches; (2) UGS supply and demand were significantly correlated with urban vitality spatial distribution; (3) these factors significantly impacted urban comprehensive vitality; (4) and there were notable UGS distribution disparities among vitality groups, with economic vitality group perceiving the greatest inequity (Gini = 0.311), followed by social vitality (Gini = 0.289) and cultural vitality group (Gini = 0.247). These findings offer valuable insights for a more refined assessment and enhancement of UGS, aiming to achieve balanced, high-quality, and sustainable urban development.

Sustainable Development Goal 11 emphasizes inclusive, resilient, and sustainable cities, in which high-quality urban green space (UGS) is essential. However, traditional two-dimensional (2D) metrics, such as Green Coverage Ratio (GCR), fail to capture the vertical complexity of UGS, leading to incomplete assessments in planning practice. With accelerating densification and land scarcity restricting horizontal expansion, sustainable UGS growth faces critical challenges. To address this gap, we developed a Three-Dimensional Green Quantity (3DGQ) mapping framework and an integrated 2D-3D assessment approach for urban green-space evaluation. We leveraged global open-source satellite data-including spaceborne LiDAR, canopy height, canopy coverage, and optical vegetation information-to retrieve growing-season Leaf Area Index (LAI) using a machine-learning model, and introduced the Three-Dimensional Green Coverage Ratio (3DGCR) to enhance 3DGQ performance assessment. Applying this framework to Beijing's central urban area, the tuned Random Forest model achieved an R² of 0.76, RMSE of 0.51, and MAE of 0.30 m²/m² for LAI retrieval, supporting 3DGQ mapping at resolutions as fine as 1 m. Results revealed a spatial divergence between 2D and 3D metrics: 3DGCR showed a "low-south, high-north" gradient, contrasting with the 2D "low-center, high-periphery" pattern. At the subdistrict level, 38.4% of units were classified as 2D-3D green imbalance under the coupling coordination framework. We identified four "2D-3D matching patterns" to provide spatial guidance for targeted UGS regeneration. Moreover, the equity assessment unveiled a "double inversion" between spatially and population-weighted results; specifically, 66.7% of subdistricts had lower population-weighted Gini coefficients for 3DGCR than for GCR. Our results further suggest that vertical UGS structural optimization in high-density cores may function as an "equity amplifier." These findings demonstrate that integrated 2D-3D assessment can uncover hidden urban green deficits and inequities overlooked by planar indicators, providing a diagnostic basis for structural optimization, equity-oriented planning, and sustainable urban green-space governance.

As a living heritage, the Jiangnan Canal landscape features interwoven natural and artificial water networks. To accurately interpret its overall spatial composition and address heritage sustainability challenges, this study proposes Geo-SegFormer: a framework for the automated segmentation of historical landscapes by integrating deep learning with multimodal geospatial data. Using a self-constructed dataset, the proposed method achieves the first 1-meter-resolution reconstruction of the entire Jiangnan Canal landscape system across 35 categories, surpassing traditional manual methods in coverage, diversity, and precision. Quantitative analysis reveals the contribution weights to the segmentation: DEM 51.6%, hydrographic data 17.7%, and imagery data 30.7%, demonstrating that the canal landscape is structured upon natural terrain, with water networks as its spatial framework. This outcome establishes a critical data foundation for heritage conservation and interdisciplinary spatial quantitative research. Beyond this specific case, the developed methodology also possesses considerable potential for cross-regional application.