Search Results (6,677)

Coastal traditional rural settlements face increasing challenges from human activities and natural disasters driven by global climate change and rapid urbanization. Ensuring their spatial stability is essential for ecological security, economic development, and social sustainability. This study addresses the lack of unified methodologies for assessing the resilience of regional traditional village clusters by proposing a “network construction–spatial simulation–resilience assessment” framework based on complex network theory. Using the Haicao housing village cluster in Rongcheng City, China, as a case study, a spatial network model was constructed, and resilience was evaluated under both current and future scenarios using eight resilience indicators. The results show that the current spatial network structure exhibits clustering with weak interconnections among subgroups. Key nodes significantly influence network metrics, resulting in low overall resilience. In future scenarios, protective measures targeting the top 15% of villages with high development potential enhanced social connections, reduced reliance on key nodes, and improved network resilience. This study provides a quantitative method for assessing the resilience of traditional village clusters and identifies critical pathways and nodes to optimize regional spatial structures. The findings offer new perspectives for guiding the preservation and sustainable development of traditional villages. Full article

Given the increasing scarcity of natural resources and the global imperative for sustainable development, the relationship between land resource allocation and green economic efficiency remains crucial but underexplored. This study, utilizing land transfer data from China Land Market Network, examines 285 cities at or above the prefectural level in China from 2007 to 2019. By applying a modified Slake-Based Measure (SBM) directional distance function model (MSBM), the study quantifies green economic efficiency and develops various panel models to investigate the impact of land resource misallocation on urban green economic efficiency. The findings indicate that land resource misallocation significantly impedes the enhancement of urban green economic efficiency. This is primarily achieved through the reduction in human capital investment and the weakening of technological conversion capabilities, both of which adversely affect the development of green economies in cities. Furthermore, government performance levels are shown to play a pivotal role in moderating the relationship between land resource misallocation and green economic efficiency, with regional heterogeneity evident between cities in old industrial bases and those in non-old industrial bases. These results underscore the critical importance of rational land resource allocation in improving green economic efficiency and facilitating the achievement of high-quality urban development. Full article

Accurate travel time prediction is essential for improving urban mobility, traffic management, and ride-hailing services. Traditional link- and path-based models face limitations due to data sparsity, segmentation errors, and computational inefficiencies. This study introduces an origin–destination (OD)-based travel time prediction framework leveraging high-resolution ride-hailing trajectory data. Unlike previous works, our approach systematically integrates spatiotemporal, quantified weather metrics and driver behavior clustering to enhance predictive accuracy. The proposed model employs a Back Propagation Neural Network (BPNN), which dynamically adjusts hyperparameters to improve generalization and mitigate overfitting. Empirical validation using ride-hailing data from Xi’an, China, demonstrates superior predictive performance, particularly for medium-range trips, achieving an RMSE of 202.89 s and a MAPE of 16.52%. Comprehensive ablation studies highlight the incremental benefits of incorporating spatiotemporal, weather, and behavioral features, showcasing their contributions to reducing prediction errors. While the model excels in moderate-speed scenarios, it exhibits limitations in short trips and low-speed cases due to data imbalance. Future research will enhance model robustness through data augmentation, real-time traffic integration, and scenario-specific adaptations. This study provides a scalable and adaptable travel time prediction framework, offering valuable insights for urban traffic management, dynamic route optimization, and sustainable mobility solutions within ITS. Full article

Three spatial turns have occurred in innovation research, including focuses on regional, urban, and intra-urban scales. The primary focus of this study was to determine the spatial distribution of innovation and the innovation networks within urban areas based on a unit-network analytical framework. ArcGIS Pro was applied to identify innovation space units and to build a collaboration matrix among these units. Subsequently, Gephi 0.9.2 was used to analyse the networks. Guangzhou was used as a case study for empirical analysis, and the main conclusions are as follows. Guangzhou contains 53 innovation space units covering 495 grids and an area of 123.75 km² (1.67% of the land area). The 53 innovation space units encompass 231,698 patents, accounting for 72.28% of the total patents in Guangzhou. The 53 innovation space units can be categorised into three levels—innovation agglomeration zones (IAZs), innovation agglomeration sub-zones (IASZs), and innovation agglomeration nodes (IANs)—which can be further classified into nine types. The spatial distribution of innovation and the innovation networks in Guangzhou form a core–periphery structure, with the Wushan–Shipai Science and Education Innovation Zone, Tianhe Centre–Yuexiu East CBD Zone, and Guangzhou Science Town Innovation Zone forming three poles at the core. The weighted degree centrality of the three poles ranked among the top 3 of the 53 innovation space units, and the link frequency between poles was among the top 3 in the 143 pairs of connections between the 53 innovation spatial units. Full article

Converting traditional villages into ecovillages provides a sustainable path for rural development by integrating ecological, social, and cultural aspects. This study utilizes the Community Sustainability Assessment (CSA) tool from the Global Ecovillage Network to evaluate the potential of six villages in Bursa, Turkey, across coastal, lowland, and mountain typologies using 21 themes and 900 criteria. Within the scope of the research, one-way analysis of variance (ANOVA) was applied to the quantitative data obtained from the CSA using IBM SPSS V28. The results indicate that coastal villages show greater potential for transition to a more sustainable lifestyle, while mountain villages face challenges with resource management and infrastructure. The villages show strong cultural and spiritual resilience, having existed for centuries, but many ecological practices have been lost due to urban migration. Recommendations include creating a national ecovillage database, training experts, supporting local projects, and convincing villagers of the transformation’s benefits. This study highlights the significance of the CSA for measuring sustainability potential and suggests future research on traditional villages in various geographies, along with developing region-specific methodologies. This approach focuses on enhancing existing villages rather than starting new ecovillages from scratch. Full article

With the intensification of global climate change, extreme precipitation events are occurring more frequently, making the monitoring and management of urban flooding a critical global issue. Urban surveillance camera sensor networks, characterized by their large-scale deployment, rapid data transmission, and low cost, have emerged as a key complement to traditional remote sensing techniques. These networks offer new opportunities for high-spatiotemporal-resolution urban flood monitoring, enabling real-time, localized observations that satellite and aerial systems may not capture. However, in low-light environments—such as during nighttime or heavy rainfall—the image features of flooded areas become more complex and variable, posing significant challenges for accurate flood detection and timely warnings. To address these challenges, this study develops an imaging model tailored to flooded areas under low-light conditions and proposes an invariant feature extraction model for flooding areas within surveillance videos. By using extracted image features (i.e., brightness and invariant features of flooded areas) as inputs, a deep learning-based flood segmentation model is built on the U-Net architecture. A new low-light surveillance flood image dataset, named UWs, is constructed for training and testing the model. The experimental results demonstrate the efficacy of the proposed method, achieving an mRecall of 0.88, an mF1_score of 0.91, and an mIoU score of 0.85. These results significantly outperform the comparison algorithms, including LRASPP, DeepLabv3+ with MobileNet and ResNet backbones, and the classic DeepLabv3+, with improvements of 4.9%, 3.0%, and 4.4% in mRecall, mF1_score, and mIoU, respectively, compared to Res-UNet. Additionally, the method maintains its strong performance in real-world tests, and it is also effective for daytime flood monitoring, showcasing its robustness for all-weather applications. The findings of this study provide solid support for the development of an all-weather urban surveillance camera flood monitoring network, with significant practical value for enhancing urban emergency management and disaster reduction efforts. Full article

In urban gas network leakage monitoring, the optimized placement of sensors plays a pivotal role in ensuring public safety and minimizing system maintenance costs. This study introduces an innovative approach that integrates hierarchical clustering with ant colony optimization (ACO) to optimize sensor layouts in urban gas networks. The hierarchical clustering technique is first employed to evaluate the strategic importance of each monitoring node, which subsequently influences the pheromone importance parameter in the ACO algorithm. Furthermore, the proposed method accounts for soil types and gas diffusion characteristics, which affect the pheromone concentration gradient, as well as the physical distances between nodes, which determine the heuristic factors in the algorithm. By finely tuning these parameters, the method achieves a significant reduction in the number of sensors required while ensuring comprehensive network coverage, thereby improving economic and operational efficiency. The optimized sensor layout not only accelerates the response to gas leaks but also enhances the system’s adaptability to complex urban environments. Simulation and field test results validate the effectiveness of this optimization approach, demonstrating its practical value in advancing the safety management of urban gas networks. Full article

This study examines how participation in the Thessaloniki International Fair (T.I.F.) influences exhibitors’ satisfaction and loyalty toward the host city of Thessaloniki. It investigates factors such as event organization, logistical support, business networking opportunities, and exhibitors’ interactions with local infrastructure and services. Using social exchange theory (S.E.T.) as a framework, this research investigates how positive exchanges, including cultural experiences, local hospitality, and professional interactions at the fair, drive destination loyalty. A mixed-methods approach was used, combining quantitative surveys and qualitative interviews with German exhibitors, to capture comprehensive insights into the factors shaping their perceptions. Results indicate high satisfaction with Thessaloniki’s cultural and culinary offerings, with local hospitality emerging as a significant factor in fostering emotional connections and loyalty intentions. However, transportation and accessibility were identified as areas for improvement, as these logistical issues detract from the overall experience and impose perceived ‘costs’ within the exchange. The findings highlight these factors’ roles in shaping destination loyalty and offer actionable recommendations for improving exhibitor experiences. The findings emphasize the broader implications for urban development, highlighting how trade fairs can serve as catalysts for city branding and infrastructural improvements, thereby strengthening Thessaloniki’s position in the competitive M.I.C.E. tourism sector. Full article

HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data. Full article

Urban parks provide areas for human wellbeing and green space benefits in densely populated landscapes but cannot accomplish all their goals in isolation. They require assistance from collaborations to address challenges. The need for these collaborations is often codified in planning documents. We assisted Rock Creek Park (National Park Service, Washington, D.C.) in their considerations of where to place “partnerships” in their strategic plan by sourcing and summarizing goal topics, hierarchies, and relationships from peer park plans. Using textual coding and network analysis approaches, we examined strategic planning documents from park system entities across the 20 largest urban areas in the United States. We found that, topically, Rock Creek Park’s five initial strategic planning goal topics—safety, access, stewardship, community engagement, and employee engagement—were common and both inward and outward-facing goals. Hierarchically, “partnerships” was routinely considered as a primary goal (a stand-alone topic) and as an integrated secondary goal (supportive within other topics). Additionally, we identified “community building” as an important, outward facing “assistance” goal, differentiated from “partnerships” in audience and encompassing how a park shows up for the urban community and demonstrates its value to the region. We discuss these findings toward urban park planning processes. Full article

Change detection for synthetic aperture radar (SAR) images effectively identifies and analyzes changes in the ground surface, demonstrating significant value in applications such as urban planning, natural disaster assessment, and environmental protection. Since speckle noise is an inherent characteristic of SAR images, noise suppression has always been a challenging problem. At the same time, the existing unsupervised deep learning-based methods relying on the pseudo labels may lead to a low-performance network. These methods are high data-dependent. To this end, we propose a novel unsupervised change detection method based on curvelet fusion and local patch similarity information clustering (CF-LPSICM). Firstly, a curvelet fusion module is designed to utilize the complementary information of different difference images. Different fusion rules are designed for the low-frequency subband, mid-frequency directional subband, and high-frequency subband of curvelet coefficients. Then the proposed local patch similarity information clustering algorithm is used to classify the image pixels to output the final change map. The pixels with similar structures and the weight of spatial information are incorporated into the traditional clustering algorithm in a fuzzy way, which greatly suppresses the speckle noise and enhances the structural information of the changing area. Experimental results and analysis on five datasets verify the effectiveness and robustness of the proposed method. Full article

The reconstruction of road networks from high-resolution satellite images is of significant importance across a range of disciplines, including traffic management, vehicle navigation and urban planning. However, existing models are computationally demanding and memory-intensive due to their high model complexity, rendering them impractical in many real-world applications. In this work, we present Cascaded Efficient Road Network (CE-RoadNet), a novel neural network architecture which emphasizes the elegance and simplicity of its design, while also retaining a noteworthy level of performance in road extraction tasks. First, a simple encoder–decoder architecture (Effi-RoadNet) is proposed, which leverages smoothed dilated convolutions combined with an attention-guided feature fusion module to aggregate features from multiple levels. Subsequently, an extended variant termed CE-RoadNet is designed in a cascaded architecture to enhance the feature representation ability of the model. Benefiting from the concise network design and the prominent representational ability of the stacking mechanism, our network can accomplish better trade-offs between accuracy and efficiency. Extensive experiments on public road datasets demonstrate that our approach achieves state-of-the-art results with lower complexity. All codes and models will be released soon to facilitate reproduction of our results. Full article

This study presents a bibliometric analysis of spatial data infrastructure (SDI) research and its application in city development. The fast urbanization and growing complexity of urban management recognize the importance of SDI in supporting sustainable urban planning and innovative city development. This study systematically reviews trends in the publications, key contributors, keywords, and thematic areas of SDI and urban settings. The study uses bibliometric tools such as VOSviewer and Biblioshiny, as well as data from 2003 to 2023. The results show that the number of publications has expanded, and the growth rate in publications has accelerated since 2013, increasing significantly due to geospatial technologies and broadening interest in the concept of smart cities. It identifies the key authors, countries, and collaborative networks that have recognized initiation in the research area. It puts forward the core contributions of Germany, Italy, and Croatia in this field. This research uses keyword co-occurrence and thematic mapping to illustrate dynamic areas of emphasis, including incorporating 3D city models with smart mapping and the application domains of Geographical Information Systems (GISs) and SDI in urban planning. This study further elaborates on other significant developing trends, such as implementing participatory sensing in environmental monitoring and securing SDI within smart city applications. It also highlights enhanced international collaborations toward strengthening the global knowledge base of the challenges in sustainable city development. Hence, this bibliometric analysis is supposed to be used for future research and policy decisions within SDI and city development. Overall, this study will support research by providing a direction for the literature on SDI and city studies and arranging bases for future studies that recommend developing urban resilience and sustainability using the effective practice of geospatial data. Full article

Change detection is an important technique that identifies areas of change by comparing images of the same location taken at different times, and it is widely used in urban expansion monitoring, resource exploration, land use detection, and post-disaster monitoring. However, existing change detection methods often struggle with balancing the extraction of fine-grained spatial details and effective semantic information integration, particularly for high-resolution remote sensing imagery. This paper proposes a high-resolution remote sensing image change detection model called FFLKCDNet (First Fusion Large-Kernel Change Detection Network) to solve this issue. FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. The model also includes a Contextual Dual-Land-Cover Attention Fusion Module (CD-LKAFM) to integrate multi-scale information during the feature recovery stage, improving the resolution of details and the integration of semantic information. Experimental results showed that FFLKCDNet outperformed existing methods on datasets such as GVLM, SYSU, and LEVIR, achieving superior performance in metrics such as Kappa coefficient, mIoU, MPA, and F1 score. The model achieves high-precision change detection for remote sensing images through multi-scale feature fusion, noise suppression, and fine-grained information capture. These advancements pave the way for more precise and reliable applications in urban planning, environmental monitoring, and disaster management. Full article

The Haidian District was, historically, rich in water resources. However, with urban development, the groundwater levels have declined, and most rivers have lost their ecological baseflows. To restore the aquatic ecosystems, the district has implemented a cyclic water network and advanced water replenishment projects. Nonetheless, the existing replenishment strategies face challenges, such as an insufficient scientific basis, lack of data, and high energy consumption. There is an urgent need to develop a scientifically robust ecological water replenishment system and optimize pump station scheduling to enhance water resource management efficiency. This study addresses the ecological water replenishment needs of seasonal rivers by integrating the Literature method, Rainfall-Runoff method, and R2cross method to develop a comprehensive approach for calculating the ecological flow and water depth. The proposed method simultaneously meets the ecological functionality and landscape requirements of seasonal rivers. Additionally, the SWMM model is employed to design intelligent pump station scheduling rules, optimizing the replenishment efficiency and energy consumption. Through field measurements and data collection, the ecological water demands of the river channels in different areas are assessed. Using a hydrodynamic model, the dynamic variations in the ecological flow and water depth are simulated. For the Cuihu, Daoxianghu, and Yongfeng areas, this study reveals that the current replenishment volume is insufficient to meet the landscape and ecological needs of the rivers. Most rivers require a 20–30% increase in water levels, with the Dazhai qu needing a substantial rise from 0.17 m to 0.3 m, representing an increase of 76%. Additionally, the results demonstrate that intelligent pump station scheduling can significantly reduce operating costs and energy consumption by dynamically adjusting the replenishment timing and flow rates. This approach optimizes the intervals between equipment activation and deactivation, thereby balancing ecological and energy-saving goals. This research not only provides technical support for the precise calculation of ecological replenishment volumes and the intelligent management of pump stations, but also offers scientific references for water resource management in similar regions. The findings will enhance the ecological functions and landscape quality of the rivers in the Haidian District while promoting refined and intelligent regional water resource management. Moreover, this study presents innovative solutions and theoretical foundations for water resource regulation under the backdrop of climate change. Full article