With the development of the urban economy and population growth, the demand for urban construction land continues to rise. Addressing the challenges of land protection and ecological security calls for the exploration of new approaches. This study emphasizes a sustainable solution by emphasizing in-depth exploration of existing land resources, moving away from the traditional “expanding the pie” model of urban development. Specifically, we selected land parcels in Shenzhen city from 2010 to 2020 that met the criteria for urban renewal and transformation as evaluation units, further categorized into residential, industrial, and commercial land for transformation. From multiple perspectives, including geological conditions, building conditions, agglomeration, social factors, and the degree of completeness of public facilities, we constructed an evaluation system comprising 23 indicators to quantify the potential for urban renewal of these units. Through the AET (AHP-EWM-TOPSIS) model analysis for assessing the potential of urban renewal and transformation, we classified the transformation parcels into different potential levels based on the optimized solutions from the model. Finally, we validated the results using the planned land units implemented in Shenzhen from 2010 to 2020, achieving an accuracy of 81% in matching the spatial distribution of potential levels with the actual situation. The comprehensive evaluation results from the model provide a basis for optimizing and enhancing sustainable urban renewal. This research contributes to the formulation of informed decisions and strategic urban development planning, enabling a more cautious and resource-efficient approach to address the challenges of urban expansion.

Most commonly used road-based homonymous entity matching algorithms are only applicable to the same scale, and are weak in recognizing the one-to-many and many-to-many types that are common in matching at different scales. This paper explores model matching for multi-scale road data. By considering the sources of various scales and landmark datasets, as well as the spatial relationships between the selected objects and the detailed features of the entities, we propose an improved matching metric, the summation product of orientation and distance (SOD), combined with the shape descriptor based on feature point vectors, the shape area descriptor based on the minimum convex hull, and three other indicators, to establish multiple multi-scale road matching models. Through experiments, the comprehensive road matching model that combines SOD, orientation, distance and length is selected in this paper. When matching the road dataset with a scale of 1:50,000 and 1:10,000, the precision, recall, and F-score of the matching result of this model reached 97.31%, 94.33%, and 95.8%, respectively. In the case that the scale of the two datasets did not differ much, we concluded that the model can be used for matching between large-scale road datasets.

The Yellow River beach area is the basic component of the Yellow River Basin. Promoting the comprehensive improvement and high-quality development of the Yellow River beach area is an important guarantee of the long-term stability of the Yellow River and an important part of promoting the high-quality development and ecological protection of the Yellow River Basin. In this paper, four new indexes (flood risk intensity index, accessibility index, biological abundance index, and remote sensing ecological index) were extracted from geospatial data and remote sensing images, and a quantitative evaluation model (Ecology-Economy -Society-Flood, EESF) for the development of the Yellow River beach area were constructed based on social statistics, such as flood control and control in the beach area. The coordinated development level of the Yellow River beach area was evaluated by combining the “CRITIC–entropy weight method” and “‘single index quantification–multi-index synthesis–multi-criteria integration’ (SMI-P)—coordination degree model” methods. The spatial autocorrelation model was used to analyze the spatial distribution characteristics of the coordinated development level, and the global sensitivity and uncertainty analysis (GSUA) was carried out for the sensitivity and uncertainty of the parameters. Taking the Yellow River beach area in Shandong Province in 2009 and 2019 as the study object, the research results showed that during this period, the coordinated development level of the Yellow River beach area in Shandong Province showed a gradual upward trend, from 0.344 to 0.580, reaching a basic coordinated state; the overall coordinated development level of the beach area showed significant autocorrelation and small spatial heterogeneity. Grain production was the most sensitive parameter in the coordinated development model of the beach area. The beach area should rationally develop and utilize agricultural resources and promote the integration of ecological industries.

Freeze–thaw erosion is one of the three major soil erosions in China, including wind erosion and hydraulic erosion, which leads to the destruction of the natural environment, the imbalance of economic development, a threat to personal safety, and irreversible disaster to the country and people. China’s permafrost area accounts for about one-fifth of the country’s land area, and the seasonal permafrost area accounts for half of China’s land area, mainly concentrated in the Qinghai–Tibet Plateau, Xinjiang Province, Heilongjiang Province, Gansu Province, and other regions. In order to establish an evaluation system for the ecological function reserve of the Greater Hinggan Mountains, nine evaluation indicators were selected from the perspectives of topography, climate, soil, and vegetation conditions. GIS technology, a multiple collinearity test, and principal component analysis were used to comprehensively evaluate the freeze–thaw erosion in the ecological function reserve of the Greater Hinggan Mountains. This study categorized the evaluation results into five intensity levels, from micro to severe. Finally, the degree of influence of different influencing factors on freeze–thaw erosion and the interactions between the factors were analyzed using a geographic detector. The results showed the following: (1) The intensity of freeze–thaw erosion in the study area gradually increased from west to east, and the comprehensive evaluation results were −0.2552 to 0.7581. Overall, moderate, severe, and mild erosion accounted for 29.83%, 25.9%, and 21.54% of the total area of the freeze–thaw zone, respectively. (2) Soil moisture content and the annual range in temperature were the main factors contributing to freeze–thaw erosion. The degree of influence of the two effects on freeze–thaw erosion (q = 0.5997) was better than that of the single-factor effect.