Pedestrian accessibility of public space is a crucial basis for ensuring public equality in sharing resources and enhancing spatial vitality and utilization efficiency. This research applied complex network theory to examine pedestrian accessibility in industrial heritage renovated public spaces, integrating the node efficiency model with an improved gravity model to propose the node accessibility model. By taking Xi’an Banpo International Art District as a case study, 13 public spaces were selected and categorized into categories to identify the current characteristics and key deficiencies. The results showed that public space pedestrian accessibility shows a positive correlation with the quality of the spaces, though individual nodes may deviate due to network effects. Correlation analyses indicated that an appropriate road setting in public spaces contributed to positive pedestrian accessibility of the whole district; however, poor spatial environment and lack of arts and cultural atmosphere were key reasons for low pedestrian accessibility. In response, four strategies for improving the pedestrian accessibility of public spaces in industrial heritage renovated districts were proposed, which included industrialization of public transport space, peripheral space integration, entrance space transition, and internal space enhancement. This study provides scientific methodology and theoretical guidance for the optimization of public space in industrial heritage renovated projects and contributes new insights into industrial heritage preservation and urban space renewal.

Forest parks are expected to balance ecological protection with high-quality ecotourism experiences, yet quantitative, multi-dimensional assessments at the regional scale remain limited. This study evaluates ecotourism experience quality across 37 national forest parks in Shaanxi Province, China, using a five-dimensional, 32-indicator framework (science popularization, landscape esthetics, ecological resources, cultural resources, and supporting conditions). Indicator weights were elicited through the Fuzzy Analytic Hierarchy Process (FAHP), and park performance was ranked via TOPSIS; key constraints were diagnosed using an obstacle degree model. Results show that landscape esthetics received the highest criterion weight (0.245), followed by ecological resources (0.215) and supporting conditions (0.195). The mean experience score across parks was 0.559, with high-quality parks concentrated in the Guanzhong region. Obstacle analysis indicates that insufficient cultural resource protection and interpretation, inadequate public service facilities, and weak science-education functions are the most persistent barriers to improving experience quality. The proposed FAHP–TOPSIS–obstacle framework supports benchmarking and priority setting for sustainable management and targeted investments in forest parks.

The land use dynamics of urban, agricultural, and ecological spaces are critical factors influencing land surface temperature (LST); however, the existing methods for describing the spatial carriers of land surface temperature evolution face issues such as granularity effects and projection sensitivity, which hinder effective comparisons across different regions and categories, thus limiting the progress of current research. This study introduces a quadtree-based spatial framework to achieve unified measurements of scale and fragmentation across Urban–Agricultural–Ecological spaces, with an empirical analysis of the Middle Yangtze River Region. Results show that between 2000 and 2020, urban and agricultural spaces expanded while ecological spaces declined, with all three types becoming increasingly fragmented. Urban agglomeration and expansion significantly elevated LST; agricultural spaces exerted relatively limited effects; and ecological fragmentation generated localized cooling but weakened core regulatory functions, ultimately leading to warming within ecological spaces themselves. This study proposes a robust method for spatial identification and fragmentation quantification, revealing the dual role of scale and morphology in regulating regional thermal environments and underscoring the importance of balanced Urban–Agricultural–Ecological configurations for climate-adaptive land use planning.

As the foundational carrier of socio-economic development and ecological security, territorial space reflects the degree of coordination between functional structure and efficiency output. However, most existing evaluation methods overlook the heterogeneous functional endowments of spatial units and therefore cannot reasonably assess the efficiency that each unit should achieve under comparable conditions. To address this limitation, this study proposes a function-oriented and interpretable framework for territorial spatial efficiency evaluation based on the Production–Living–Ecological (PLE) paradigm. An entropy-weighted indicator system is constructed to measure production, living, and ecological efficiency, and an XGBoost–SHAP model is developed to infer the nonlinear mapping between functional attributes and efficiency performance and to estimate the ideal efficiency of each spatial unit under Quanzhou’s prevailing macro-environment. By comparing ideal and observed efficiency, functional–efficiency deviations are identified and spatially diagnosed. The results show that territorial efficiency exhibits strong spatial heterogeneity: production and living efficiency concentrate in the southeastern coastal belt, whereas ecological efficiency dominates in the northwestern mountainous region. The mechanisms differ substantially across dimensions. Production efficiency is primarily driven by neighborhood living and productive conditions; living efficiency is dominated by structural inheritance and strengthened by service-related spillovers; and ecological efficiency depends overwhelmingly on local ecological endowments with additional neighborhood synergy. Approximately 45% of spatial units achieve functional–efficiency alignment, while peri-urban transition zones and hilly areas present significant negative deviations. This study advances territorial efficiency research by linking functional structure to efficiency generation through explainable machine learning, providing an interpretable analytical tool and actionable guidance for place-based spatial optimization and high-quality territorial governance.

Remote sensing (RS) image change detection (CD) is pivotal for urban planning, non-agricultural land monitoring, and damage assessment. At present, CD models typically rely on a large amount of high-quality labeled data. Nevertheless, under limited-data scenarios, they generally suffer from insufficient feature representation and inadequate deep semantic supervision, resulting in degraded CD performance. Motivated by the above limitations, a hierarchically stabilized cross-scale feature learning network (HSCFLNet) is presented. A convolution-attention gated fusion module (CAGF) is initially proposed. This module enhances change feature learning via a convolution-attention fusion mechanism, thereby effectively mitigating noise interference and strengthening change-related feature discrimination under limited-data conditions. Secondly, a ReLU-KAN-based deep supervision module (RKDS) is incorporated to promote cross-scale semantic consistency, thereby effectively alleviating the difficulty of deep semantic representation learning under limited-data settings. Finally, the proposed method is systematically evaluated under both large-scale and limited-data settings. Experimental results demonstrate that HSCFLNet achieves state-of-the-art (SOTA) performance on large-scale datasets, including LEVIR-CD and SYSU-CD, as well as on the limited-data dataset CL-CD. Furthermore, multiple random sampling experiments conducted under the constructed limited-data scenarios verify the stability and performance consistency of the proposed model under extremely limited-data conditions. The code will be available at https://github.com/entereverything/HSCFLNet.