Understanding the spatial distribution and associated factors of sustainable environmental development (SED) is crucial for effectively formulating action plans aimed at achieving the environment-related Sustainable Development Goals (SDGs). Because of spatial heterogeneity characterized by non-uniform distributions of SED within a region, the use of a single or aggregated value at the national or provincial scale in existing research obscures internal variabilities and fails to adequately reveal the spatial disparities in the progress of environment-related SDGs. Consequently, this study aims to investigate the spatially heterogeneous patterns of SED in prefecture-level cities within the Yangtze River Economic Belt (YREB), China—a critical economic zone characterized by stark intra-regional disparities and pressing environmental challenges. To achieve this objective, the SED index is first constructed using principal component weighted aggregation to quantify the SED status, local Moran’s I is then employed to identify heterogenous patters of the spatial distribution patterns of SED, and geographical random forest is utilized to explore spatially varying association patterns between SED and influences factors. In the YREB, the findings indicate the following key insights: (1) SED has generally shown a positive trend across most cities from 2013 to 2021, with approximately 30% experiencing a downward trend, particularly in Jiangxi, Hubei, and Hunan Provinces; (2) the number of high- or low-value aggregation clusters has decreased, concurrent with an increase in spatial variability; (3) human activity disturbance and population density have been identified as significant factors influencing SED, with a notable impact in cities across Sichuan, Chongqing, Guizhou, and Hubei Provinces. This research contributes technical support and a scientific foundation for evaluating and enhancing SED.

Multi-temporal comparative analysis of stone inscription images helps understand weathering processes and enables more targeted conservation measures, with accurate image registration serving as the prerequisite for such analysis. However, existing registration methods perform poorly on these images due to sparse and unevenly distributed feature points in text regions caused by natural weathering processes such as weathering and exfoliation. This study proposes a semantic-aware registration framework for multi-temporal stone inscription images, implementing a four-stage processing workflow: global initial registration using Scale-Invariant Feature Transform (SIFT) algorithm with geometric filtering; region partitioning through grid division and text region identification; sub-block enhancement applying SIFT-Accelerated KAZE (AKAZE) dual-feature fusion strategy for regions with insufficient matching points; and global fine registration integrating multi-source matching points with Random Sample Consensus (RANSAC) optimization. Experiments on stone inscriptions with varying degrees of weathering validate the effectiveness of this method: for well-preserved samples, the Mean Euclidean Error (MEE) decreased by 94.8% and structural similarity improved by 813.7%; for severely weathered samples, matching points in text regions increased 18-fold with an inlier ratio reaching 97.35%. This research represents the integration of text region semantic recognition with adaptive feature enhancement, providing reliable technical support for multi-temporal stone inscription analysis and cultural heritage conservation.