Search Results (74)

Street networks provide an invaluable source of information about the different temporal and spatial patterns emerging in our cities. These streets are often represented as graphs where intersections are modeled as nodes and streets as edges between them. Previous work has shown that raster representations of the original data can be created through a learning algorithm on low-dimensional representations of the street networks. In contrast, models that capture high-level urban network metrics can be trained through convolutional neural networks. However, the detailed topological data is lost through the rasterization of the street network, and the models cannot recover this information from the image alone, failing to capture complex street network features. This paper proposes a model capable of inferring good representations directly from the street network. Specifically, we use a variational autoencoder with graph convolutional layers and a decoder that generates a probabilistic, fully connected graph to learn latent representations that encode both local network structure and the spatial distribution of nodes. We train the model on thousands of street network segments and use the learned representations to generate synthetic street configurations. Finally, we proposed a possible application to classify the urban morphology of different network segments, investigating their common characteristics in the learned space. Full article

Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle emissions, mitigate urban heat island effects, and enhance the resilience of green infrastructure in peri-urban contexts. Using Deir Ghbar, a rapidly developing marginal area on Amman’s western edge, as a case study, we combine objective walkability metrics (street connectivity and residential and retail density) with GIS-based spatial regression analysis to examine relationships with residents’ sense of community. Employing a quantitative, correlational research design, we assess walkability using a composite objective walkability index, calculated from the land-use mix, street connectivity, retail density, and residential density. Our results reveal that higher residential density and improved street connectivity significantly strengthen social cohesion, whereas low-density zones reinforce spatial and socioeconomic disparities. Furthermore, the findings highlight the potential of targeted green infrastructure interventions, such as continuous street tree canopies and permeable pavements, to enhance pedestrian comfort and urban ecological functions. By visualizing spatial patterns and correlating built-environment attributes with community outcomes, this research provides actionable insights for policymakers and urban planners. These strategies contribute directly to several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by fostering more inclusive, connected, and climate-resilient neighborhoods. Deir Ghbar emerges as a model for scalable, GIS-driven spatial planning in rural and marginal peri-urban areas throughout Jordan and similar regions facing accelerated urban transitions. By correlating walkability metrics with community outcomes, this study operationalizes SDGs 11 and 13, offering a replicable framework for climate-resilient urban planning in arid regions. Full article

Due to rising temperatures, energy use, and thermal discomfort, urban heat islands (UHIs) pose a serious environmental threat to urban sustainability. This systematic review synthesizes peer-reviewed literature on various forms of green infrastructure and their mechanisms for mitigating UHI effects, and the function of urban green spaces (UGSs) in reducing the impact of UHI. In connection with urban parks, green roofs, street trees, vertical greenery systems, and community gardens, important mechanisms, including shade, evapotranspiration, albedo change, and ventilation, are investigated. This study emphasizes how well these strategies work to lower city temperatures, enhance air quality, and encourage thermal comfort. For instance, the findings show that green areas, including parks, green roofs, and street trees, can lower air and surface temperatures by as much as 5 °C. However, the efficiency of cooling varies depending on plant density and spatial distribution. While green roofs and vertical greenery systems offer localized cooling in high-density urban settings, urban forests and green corridors offer thermal benefits on a larger scale. To maximize their cooling capacity and improve urban resilience to climate change, the assessment emphasizes the necessity of integrating UGS solutions into urban planning. To improve the implementation and efficacy of green spaces, future research should concentrate on policy frameworks and cutting-edge technology such as remote sensing. Full article

The Façade Opening Rate (FOR) reflects a building’s capacity to withstand seismic loads, serving as a crucial foundation for seismic risk assessment and management. However, FOR data are often outdated or nonexistent in rural areas, which are particularly vulnerable to earthquake damage. This paper proposes a practical framework for estimating FORs from real-scene 3D models derived from UAV photogrammetry. The framework begins by extracting individual buildings from 3D models using annotated roof outlines. The known edges of the roof outline are then utilized to sample and generate orthogonally projected front-view images for each building façade, enabling undistorted area measurements. Next, a modified convolutional neural network is employed to automatically extract opening areas (windows and doors) from the front-view façade images. To enhance the accuracy of opening area extraction, a vanishing point correction method is applied to open-source street-view samples, aligning their style with the front-view images and leveraging street-view-labeled samples. Finally, the FOR is estimated for each building by extracting the façade wall area through simple spatial analysis. Results on two test datasets show that the proposed method achieves high accuracy in FOR estimation. Regarding the mean relative error (MRE), a critical evaluation metric which measures the relative difference between the estimated FOR and its ground truth, the proposed method outperforms the closest baseline by 5%. Moreover, on the façade images we generated, the MRE of our method was improve by 1% and 2% compared to state-of-the-art segmentation methods. These results demonstrate the effectiveness of our framework in accurately estimating FORs and highlight its potential for improving seismic risk assessment in rural areas. Full article

Tunnels are commonly found in small and enclosed environments on highways, roads, or city streets. They are constructed to pass through mountains or beneath crowded urban areas. To prevent accidents in these confined environments, lane changes, slow driving, or speeding are prohibited on single- or multi-lane one-way roads. We developed a foreground detection algorithm based on the K-nearest neighbor (KNN) and Gaussian mixture model and 400 collected images. The KNN was used to gather the first 200 image data, which were processed to remove differences and estimate a high-quality background. Once the background was obtained, new images were extracted without the background image to extract the vehicle’s foreground. The background image was processed using Canny edge detection and the Hough transform to calculate road lines. At the same time, the oriented FAST and rotated BRIEF (ORB) algorithm was employed to track vehicles in the foreground image and determine positions and lane deviations. This method enables the calculation of traffic flow and abnormal movements. We accelerated image processing using xfOpenCV on the PYNQ-Z2 and FPGA Xilinx platforms. The developed algorithm does not require pre-labeled training models and can be used during the daytime to automatically collect the required footage. For real-time monitoring, the proposed algorithm increases the computation speed ten times compared with YOLO-v2-tiny. Additionally, it uses less than 1% of YOLO’s storage space. The proposed algorithm operates stably on the PYNQ-Z2 platform with existing surveillance cameras, without additional hardware setup. These advantages make the system more appropriate for smart traffic management than the existing framework. Full article

This study examines the gender dynamics in urban space usage within the historic city center of Tebessa, Algeria, exploring how cultural factors and street networks influence gender-specific pedestrian behavior and land use patterns. Using a multidisciplinary approach combining space syntax techniques, GIS analysis, and behavioral data collection, we analyzed the relationships between street networks, land use attractors, and gender-differentiated pedestrian flows. Key findings reveal significant differences in spatial navigation patterns between men and women, influenced by cultural norms and gender-specific land use distribution. Women’s movement is more constrained and focused on specific attractors, while men navigate the entire urban system more freely. The study also highlights the impact of “edge effects”, where extramural attractors strongly influence intramural gender movement, particularly for women. These gender-specific patterns often override street network influences predicted by traditional space syntax theories. Our research contributes to the understanding of sustainable urban development in culturally rich contexts by demonstrating the need for gender-inclusive planning that considers local cultural practices. The findings have important implications for urban planners and policymakers working to create more equitable and functional historic city centers while preserving cultural heritage and addressing gender-specific needs. Full article

Despite extensive research on what draws people to urban streets, most existing insights originate from Western contexts, offering limited perspectives from wider urban contexts. This study addresses this gap by examining everyday street activities in Chinese urban villages, focusing specifically on how two spatial scales, the entire street edge and territorial segments, influence necessary, optional, and social engagements. Drawing on video recordings and walk-by observations in two urban villages in Wuhan, China, the research systematically measured the type and duration of activities across 110 territorially defined segments. The findings reveal that territorial segments, i.e., smaller-scale personalised subdivisions at a micro-scale often shaped by bottom–up adaptations, exert a significantly stronger influence upon how people use and linger in street space rather than entire street edges at a macro-scale, which shows only limited impact. This underscores the importance of fine-grained socio-spatial design and local ownership in fostering vibrant people-centred streets. By demonstrating the decisive role of micro-scale features, which span storefront layouts, semi-public alcoves, and adaptive uses, these results carry important implications for urban practitioners seeking to balance top–down redevelopment with bottom–up initiatives. Ultimately, the study enriches the global discourse on street-edge understanding and design, emphasising that territorial segments can be powerful catalysts for promoting activity and community life in dense urban contexts. Full article

The spatial configuration of urban areas impacts environmental sustainability, social equity, and economic and social resilience. This study examines the intricate relationship between spatial arrangements and the planning and design of BREEAM Outstanding projects in London, UK. It analyses the relationship between urban morphology and the effectiveness of sustainable building practices and contributes to the broader objectives of urban sustainability. This research focuses on London, UK—a city renowned for its complex urban fabric and architectural heterogeneity—using a multi-case study approach to dissect the elements that facilitate the development of BREEAM Outstanding projects. This study analyses key spatial characteristics such as land use diversity, subway network analysis, and street network analysis using betweenness centrality of edges and node degrees. These factors are considered due to their impact on energy performance, carbon emissions, and social sustainability metrics. Furthermore, this research explores how urban design strategies, such as enhanced walkability and mixed-use development, reinforce the success of BREEAM-certified Outstanding-rated projects. The findings of this investigation reveal a correlation between urban environments and the development of BREEAM Outstanding-rated projects in London. By aligning the spatial organisation of urban form with BREEAM principles, urban planners, policymakers, and architects can facilitate the creation of cities that are environmentally sustainable, socially inclusive, and economically prosperous. The research offers substantive insights and actionable recommendations for future urban development, advocating for a comprehensive and interdisciplinary approach to sustainable city planning and design. The spatial arrangement of urban form impacts the planning and design of BREEAM Outstanding projects. Findings from current and future research will be used to investigate the connections between spatial arrangement and various categories in BREEAM and how they can influence future sustainable urban environments to set a benchmark for sustainability for contributing to a more equitable urban future. Full article

Buffer zone delineation often extends from the outermost edge of a site boundary for a specific distance. This study proposes a novel approach to determining the visual buffer for the temple town of Chiang Mai city. Adjacent Visual Buffer (AVB) was determined for the temples and their approaching routes using a GIS-based visibility method based on the viewing feature’s visual coverage and the observer’s visual range. The findings revealed that the total viewshed/visual range characterized the visibility of the temples in relation to the viewing feature’s height, resulting in AVB radii of varying sizes. The highest AVB radius of more than 200 m was found for temples situated in the city’s core, followed by those located on the city’s main streets and in isolated areas. The approaching route buffer was determined as a radius of 25 m from the road’s center. Interestingly, the density map results were consistent with the temple buffer results, indicating that the main roads of Chiang Mai’s historic area are highly used as an approaching route for temples. Combining the visual buffers of both temples and their approaching routes can aid in determining the level of control or guideline requirements in specific roads and areas. Full article

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs. Full article

In rapid urbanization, the socio-spatial environment between inner-city functional areas faces numerous challenges. Assessing and enhancing the environmental quality of these areas has become an urgent research issue. This study quantitatively evaluates the social-spatial environment of inner-city transitional edges, selecting Chongqing’s Yuzhong District as the case study area. It explores the relationship between spatial environmental factors and social activities. Integrating spatial data, internet “big” data, and field survey data, a multidimensional evaluation of the quality of the social-spatial environment framework is constructed, encompassing four dimensions: connectivity, social function, comfort, and conviviality. Subsequently, a multiple linear regression model is used to explore the main environmental factors influencing social activities on transitional edges. The results show that the density of street trees, lighting facilities, functional density, and functional diversity significantly impact social activities, demonstrating the correlation between the spatial environment of inner-city transitional edges and social activities. Corresponding optimization strategies for each dimension in transitional edges are then summarized. This study provides references for coordinating inner-city functional areas, optimizing urban environments, and promoting sustainability. It can also be applied to a broader range of transitional edge evaluation studies. Full article

In the face of diverse chronic pressures and increased factor mobility, the resilience of urban internal network structures has become a cutting-edge research topic. This study utilizes 2019 mobile signaling big data to construct employment and recreational flow networks among 101 townships and streets within Nanjing City. Based on the characteristics of these network structures, the resilience of the network structure is measured from the perspectives of density, symmetry, and transmissibility through interruption simulation techniques. The results show that the intensity of population mobility within Nanjing presents a general decay from the central urban area to the outer layers. In the employment scenario, cross-river population mobility is more frequent, while in the recreational scenario, the natural barrier effect of the Yangtze River is prominent. Due to the concentration of employment centers and high spatial heterogeneity, the employment flow network exhibits greater vulnerability to sudden shocks. Townships and streets with weighted degree values ranking around 60 and 80 are of great importance for maintaining the normal operation of both employment and recreational flow networks. Strengthening the construction of resilient parks and village planning within resilient cities can enhance the risk resistance of employment and recreational flow networks. Full article

In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. Therefore, this paper, based on a large, publicly available urban street tree dataset, proposes YOLO-SegNet for individual street tree segmentation. In the first stage of the street tree object detection task, the BiFormer attention mechanism was introduced into the YOLOv8 network to increase the contextual information extraction and improve the ability of the network to detect multiscale and multishaped targets. In the second-stage street tree segmentation task, the SegFormer network was proposed to obtain street tree edge information more efficiently. The experimental results indicate that our proposed YOLO-SegNet method, which combines YOLOv8+BiFormer and SegFormer, achieved a 92.0% mean intersection over union (mIoU), 95.9% mean pixel accuracy (mPA), and 97.4% accuracy on a large, publicly available urban street tree dataset. Compared with those of the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPNet), UNet, DeepLabv3+, and HRNet, the mIoUs of our YOLO-SegNet increased by 10.5, 9.7, 5.0, 6.8, 4.5, and 2.7 percentage points, respectively. The proposed method can effectively support smart agroforestry development. Full article

Globalization and informatization have exerted far-reaching impacts on the spatial connection and development of urban systems. This study, concerning the network of an urban system based on the space of flows, supplements the insufficiency on the micro-level in macro-urban network research. Taking Nanjing as an example, this study explores the characteristics of the network of the urban system from the perspective of people flow, refining the granularity of the analysis to the township- and street-level spatial units using mobile phone data. The findings are as follows: (1) There is a multicenter layered network pattern, with the main urban area being the core of the network, while Dongshan Street and Moling Street, as secondary centers, form a joint development pattern with the main urban area. (2) The spatial differentiation is significant. The spatial heterogeneity of “centralization in the central region, delayering in the north, and hierarchization in the south” is obvious. The net people inflow nodes are mainly concentrated in the main urban area and its surroundings, while the net outflow nodes are mostly located on the edge of the city. Moreover, the nodes to the south of Yangtze River are advantageous in urban resource control. (3) The phenomenon of “double shadow circle” appears in the ring of the main city and the ring of the municipal area. Moreover, the northern district experiences a serious outflow of population. (4) The effect of policy intervention is beginning to show. Improved levels of development of street and township units such as Jiangbei New District show the positive influence of national strategy on regional development. Full article

As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat 5/Thematic Mapper images to effectively and accurately identify small urban water bodies. The findings indicate that the trained U-net convolutional neural network (U-Net) water body extraction model and loss function combining Focal Loss and Dice Loss adopted in this study demonstrate high precision in identifying water bodies within the main urban area of Hangzhou, with an accuracy rate of 94.3%. Trends of decrease in water areas with a continuous increase in landscape fragmentation, particularly for the plain river network, were observed from 1985 to 2010, indicating a weaker connection between water bodies resulting from rapid urbanization. Large patches of water bodies, such as natural lakes and big rivers, located at divisions at the edge of the city are susceptible to disappearing during the rapid outward expansion. However, due to the limitations and strict control of development, water bodies, referring to as wetland, slender canals, and plain river networks, in the traditional center division of the city, are preserved well. Combined with the random forest classification method and the U-Net water body extraction model, land use changes from 1985 to 2010 are calculated. Reclamation along the Qiantang River accounts for the largest conversion area between water bodies and cultivated land, constituting more than 90% of the total land use change area, followed by the conversion of water bodies into construction land, particularly in the northeast of Xixi Wetland. Notably, the conversion of various land use types within Xixi Wetland into construction land plays a significant role in the rise of the carbon footprint. Full article