Park green space is an important part of the green infrastructure in a city, and urban park space accessibility is an important indicator for measuring the city’s comprehensive strength, living environment, and resident happiness, which greatly affects the sustainable development and construction of the city. The spatial syntax method can discuss the accessibility of urban parks from the perspective of topology, which inherits the objective stability of the topological relationship. There are few research studies on the accessibility of urban parks. Therefore, with the support of space syntax theory and the spatial statistics method, this paper combined the data of park green space and road network to complete the accessibility evaluation of urban parks in the Changsha City urban area. This paper evaluates the accessibility of urban parks in the study area from four aspects, namely global accessibility, perceptual accessibility, local accessibility, and psychological accessibility, by using five quantitative indexes of space syntax, namely connectivity, depth, integration, selection, and synergy. The conclusions are as follows: The spatial layout area of the park is roughly consistent with the areas that have good global and local accessibility of the road network, and the global accessibility and local accessibility of the park in the study area are relatively high. The global spatial structure can be better perceived by the local space at a radius scale greater than or equal to 3000 m, and the perceptual accessibility of the park is high. Most of the urban parks in the Changsha urban area are located in sections with high space efficiency. The park space penetration is good, and nearly 70% of the parks have high psychological accessibility. Based on the overall spatial layout of urban parks, the space syntax method quantitatively and comprehensively evaluates the accessibility of urban parks from the perspective of topology. The conclusion is reliable and has important application value in the evaluation of park accessibility. It can be used as an important supplement to improve the evaluation model of accessibility.

Exploring the heterogeneous characteristics of the urban expansion process is essential for understanding the dynamics of the urban spatial structure. Many studies focused on depicting the spatio-temporal characteristics based on urban expansion patches. However, measuring heterogeneous characteristics of urban expansion from agglomeration areas comprising the expanded urban construction land patches have not been adequately explored. This study presents a novel approach and two improved indices for characterizing the heterogeneity of urban spatial agglomeration areas during urban expansion. Firstly, we proposed a Gaussian mixture model considering multiple constrains and density-based spatial clustering of applications with noise (DBSCAN) integration method to identify and extract the urban agglomeration areas automatically. Secondly, the gradient analysis and the compact index using the inverse “S” function are introduced to explore the spatio-temporal characteristics from a macrocosmic perspective. Finally, the compactness index (NCI) and normalized dispersion index (NDIS) are improved based on agglomeration area data. The microcosmic heterogeneous characteristics are measured by these two improved indices and the positional offset characteristics indices (POCIS). The method was implemented in the urban area of Changsha, Hunan Province, China in 2005, 2010, and 2015. The results show that (1) compared to that in the Changsha City Master Plan (2003–2020), the recognition rate was higher in the agglomeration areas than others. (2) The overall expansion trend in Changsha transitioned toward decentralization, making Changsha a polycentric city. (3) The agglomeration of urban expansion in the east-west direction became compact; that in the north-south direction became looser; most clusters expanded to the west and a new sub-center would appear. The proposed method can effectively characterize their heterogeneity, which can provide valuable references for urban planning and policymaking.

Electric vehicles (EVs) have attracted considerable attention because of their clean and high-energy efficiency. Reasonably planning a charging station network has become a vital issue for the popularization of EVs. Current research on optimizing charging station networks focuses on the role of stations in a local scope. However, spatial features between charging stations are not considered. This paper proposes a charging station selection method based on the graph convolutional network (GCN) and establishes a charging station selection method considering traffic information and investment cost. The method uses the GCN to extract charging stations. The charging demand of each candidate station is calculated through the traffic flow information to optimize the location of charging stations. Finally, the cost of the charging station network is evaluated. A case study on charging station selection shows that the method can solve the EV charging station location problem.

The Segment Anything Model (SAM) enables zeroshot or interactive segmentation of visual content and has shown remarkable performance with natural images. However, SAM often fails to accurately extract roads from high-resolution remote sensing imagery (HRSI) due to complex road boundaries, background interference, and its semantic-agnostic nature. This paper proposes an automated road extraction method called Trajectory Points Prompting SAM (TPP-SAM), which uses trajectory points as segmentation prompts for roads in HRSI, eliminating the need for complex manual prompts. TPPSAM introduces road centerline constraint (RCC) and sampling constraint (SC) layers to select reliable trajectory prompt points and migrates centroid information from building roofs as a background constraint (BC). This three-layer constraint system integrates real-world crowdsourced information with the vision foundation model. Additionally, the data structure of prompt points is organized using map matching and recoding rules, accounting for their attributes and relative positional coordinates. The processed data are then embedded into the prompt encoder to generate road semantic masks. Results show that TPPSAM achieves comprehensive road segmentation across various scenarios, demonstrating the viability of using trajectory points as interactive road segmentation prompts. The proposed model extends the application of geographic image segmentation and holds potential for other geolabeled features.

To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.

GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method.