Search Results (4,449)

The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, CubeSats, and terrestrial infrastructures that comprise the framework of Space–Aerial–Ground Integrated Networks (SAGINs) as vital enablers of the International Mobile Telecommunications (IMT)-2030 standards. This paper examines the role of UAVs in providing flexible and quickly deployable airborne connectivity. It also discusses how CubeSats enhance global coverage through low-latency relaying and resilient backhaul links from low Earth orbit (LEO). Additionally, the paper highlights how terrestrial systems contribute high-capacity, densely concentrated communication layers that support various end-user applications. By examining their interoperability and coordinated resource allocation, the paper underscores that the seamless interaction of SAGIN nodes is essential for achieving the ultra-reliable, intelligent, and pervasive communication capabilities envisioned by IMT-2030. As 6G aims for ultra-low latency, high reliability, and massive connectivity, UAVs and CubeSats emerge as key enablers for extending coverage and capacity, particularly in remote and dense urban regions. Furthermore, the role of large language models (LLMs) is explored for intelligent network management and real-time data optimization, while quantum communication is analyzed for ensuring security and minimizing latency. The integration of LLMs into quantum-enhanced edge intelligence for SAGINs represents an emerging research frontier for adaptive, high-throughput, and context-aware decision-making. By exploiting quantum-assisted parallelism and entanglement-based optimization, LLMs enhance the processing efficiency of multimodal data across space, aerial, and terrestrial nodes. This paper further investigates distributed quantum inference and multimodal sensor data fusion to enable resilient, self-optimizing communication systems comprising a high volume of data traffic, which is a critical bottleneck in the global connectivity transition. LLMs are envisioned as cognitive control centers capable of generating semantic representations for mission-critical communications that enhance energy efficiency, reliability, and adaptive learning at the edge. The findings of the survey reveal that quantum-enhanced LLMs overcome challenges pertaining to bandwidth allocation, dynamic routing, and interoperability in existing classical communication systems. Overall, quantum-empowered LLMs significantly assist intelligent, autonomous, and immersive communications in SAGIN, while enabling secure, privacy-preserving communication. Full article

Monitoring urban subsidence over ultra-long periods using time-series Interferometric synthetic aperture radar (InSAR) technology is critically important. Conventional approaches, however, face two main limitations: significant atmospheric phase residuals in complex urban settings, and discontinuous temporal time-series with short temporal coverage due to single-platform data constraints. To address these limitations, this study presents a new method for estimating ultra-long-term subsidence time series in urban areas, which combines Interferometric Subset Stacking (ISS) with multi-platform data fusion (DF). The methodology firstly processes TerraSAR-X and Sentinel-1A datasets through differential interferometry and applies ISS for atmospheric phase suppression. Next, bilinear interpolation unifies the spatial resolution and aligns the spatial reference frames of the two datasets. Subsequently, joint modeling derives subsidence velocities. Finally, temporal integration via linear interpolation and moving averaging produces a unified spatio-temporal deformation sequence. Applied to the Beijing region, China, this approach generated a 12-year ultra-long-term subsidence time series result (2012–2024), revealing maximum cumulative subsidence of 1100 mm spatially correlated with groundwater extraction patterns. Validation against Global Navigation Satellite System (GNSS) data showed strong agreement (correlation coefficient: 0.94, Root Mean Square Error (RMSE): 6.3 mm). The method achieved substantial atmospheric reduction—67.7% for Sentinel-1A and 24.1% for TerraSAR-X—representing approximately 15–20% accuracy improvement over conventional Generic Atmospheric Correction Online Service (GACOS) for InSAR. By effectively utilizing multi-platform data, this approach makes fuller use of the available phase information and compensates for the temporal gaps inherent in single-satellite datasets. It thus offers a valuable framework for long-term urban deformation monitoring. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

In the context of “liquid modernity,” older adults’ social inclusion is challenged by weakening support networks. This study examines generativity—the capacity to contribute to future generations—as a vital component of active aging. The aim is to analyze generative interests and opportunities to bridge the structural deficit that prevents seniors from being recognized as active social subjects. Adopting a qualitative single-case-study design, research was conducted via focus groups with 17 retired adults (aged 65–75 years) from urban and rural settings in Jaén, Spain. Data collection followed a semi-structured script, and transcripts were systematically processed using ATLAS.ti 23 software for thematic content analysis to ensure methodological rigor. The results indicate a significant interest in mentorship and transmitting “experiential wisdom”. However, systemic barriers such as agism and a lack of adapted institutional channels constitute a “structural lag”. These obstacles hinder the transition of generative desire into concrete social action, often resulting in wasted human and social capital. The study proposes “Senefficiency” (Planned Generative Efficacy) as a strategic model to transform senior potential into active social capital. It advocates for public policies to transition from welfare-based objectives toward creating formal channels for sociopolitical participation, ensuring that older adults’ contributions are recognized within sustainable community development. Full article

Precise assessment of the vulnerability characteristics of human–land systems is es-sential for ensuring ecological security and sustainable development in regions affected by large-scale engineering projects. Using the Three Gorges Reservoir Area as a case study, this research develops a comprehensive evaluation index system based on a coupled framework of “Geo-environmental Background—Ecosystem Structure—Anthropogenic Perturbation.” By integrating deep neural networks (DNN), convolutional neural networks (CNN), and the analytic hierarchy process (AHP) with multi-source data, we perform a thorough assessment of eco-geological vulnerability. The results reveal the following key findings: (1) In eco-geological vulnerability assessment, deep learning methods (DNN and CNN) significantly outperform traditional AHP, with CNN showing superior precision and specificity due to its ability to extract local spatial features effectively, while DNN exhibits stronger overall robustness. (2) The spatial distribution of eco-geological vulnerability in the reservoir area is notably heterogeneous, with high and Extreme vulnerability zones concentrated along the main riverbanks, major tributary estuaries, and urban peripheries. These zones are strongly coupled with steep terrain, erodible lithology, high geological hazard risks, and intensive human activity. (3) Although the overall vulnerability remains relatively stable, local sensitivity is increasing. Ecological restoration projects in mountainous regions have effectively mitigated vulnerability in the hinterlands, while rapid urbanization has exacerbated vulnerability in emerging urban areas. The study concludes that the spatial pattern of vulnerability is primarily influenced by the geological–ecological background, with human disturbance—especially land use intensity—acting as the primary driver of vulnerability dynamics and local hotspots of high vulnerability. Based on these findings, we recommend a differentiated management approach tailored to eco-geological units: for high and extreme vulnerability zones along river and urban corridors, efforts should focus on spatial constraints and systemic resto-ration; for low and negligible vulnerability zones in mountainous areas, strategies should aim to enhance ecosystem quality and stability, thus fostering a coordinated regional ecological security framework. Full article

Air pollution remains a major global public health concern, with fine particulate matter (PM_2.5) recognized as an important environmental risk factor for lung cancer. This ecological study assessed lung cancer mortality attributable to long-term PM_2.5 exposure in the 26 Brazilian state capitals and the Federal District (Brasília) from 2014 to 2023. Annual mean PM_2.5 concentrations were estimated using reanalysis-based PM_2.5 concentration estimates and atmospheric reanalysis data, ensuring consistent spatial and temporal coverage. Mortality data were obtained from the Brazilian Mortality Information System (SIM/DATASUS). Health impacts attributable to PM_2.5 exposure were estimated using the World Health Organization’s AirQ+ model, based on exposure–response functions from the Global Burden of Disease framework. During the study period, 97.41% of annual PM_2.5 means exceeded the WHO Air Quality Guideline of 5 µg/m³, and 28.52% surpassed the current Brazilian regulatory limit. Higher concentrations were observed mainly in capitals from the North and Southeast regions, reflecting the influence of biomass burning, urbanization, and regional atmospheric processes. Approximately 13.56% of lung cancer deaths in Brazilian capitals were attributable to PM_2.5 exposure, with the highest absolute numbers concentrated in the Southeast region. These findings demonstrate a substantial and spatially heterogeneous lung cancer burden associated with urban air pollution in Brazil and highlight the need for strengthened air quality management and targeted urban public health policies. Full article

Despite recent major advances in autonomous driving, several challenges remain. Even with modern advanced sensors and processing systems, vehicles are still unable to detect all possible obstacles present in complex urban settings and under diverse environmental conditions. Consequently, numerous studies have investigated artificial intelligence methods to improve vehicle perception capabilities. This paper presents a new methodology using a framework named CarAware, which fuses multiple types of sensor data to predict vehicle positions using Deep Reinforcement Learning (DRL). Unlike traditional DRL applications centered on control, this approach focuses on perception. As a case study, the PPO algorithm was used to train and evaluate the effectiveness of this methodology. Full article

Sustainable land governance requires timely and accurate monitoring of land-use change to balance ecological, agricultural, and urban demands. Yet policymakers rarely receive actionable insights fast enough, because large-scale geospatial computation and rapid delivery remain disconnected. To close this gap, we introduce a Computational-Visualization Co-design (CVC) framework that welds a distributed high-performance computing engine to a real-time, preprocessing-free visualization system. Our approach represents a system-level innovation. It co-designs computational shards as visualization units, eliminating intermediate data reorganization. This co-design paradigm makes analytical results immediately visible. CVC processes a 20 TB imagery dataset and overlays millions of parcels 5–9 times faster than conventional engines. Map service publishing plummets from 168 h to just 7—a 24-fold speed-up—while client-side performance stays robust. The framework directly supports sustainable land management. It enables proactive monitoring, rapid impact assessment, and evidence-based policy formulation. Our work thus contributes to key Sustainable Development Goals related to land and cities. Validated with national survey data from China, the system merges analysis with instantaneous visual feedback, offering a practical route to sustainable land governance. Full article

Urbanization in developing countries has intensified ecological degradation and reduced the availability of Urban Green Spaces (UGS), including in Bogor City, Indonesia, where public UGS covers only 4.26%—far below the national minimum requirement of 20%. Agroforestry is increasingly recognized as a viable strategy to enhance the ecological, economic, and social functions of limited urban green areas. This study assesses the sustainability of agroforestry practices in Bogor City’s public UGS using the Multi-Aspect Sustainability Analysis (MSA) method across five aspects: ecological, economic, social, infrastructure–technology, and legal–institutional. This study is grounded in three principal hypotheses: (i) the implementation of agroforestry exerts a positive effect on ecological, social, and infrastructural–technological sustainability; (ii) economic and legal–institutional dimensions constitute the major limiting factors affecting overall sustainability performance; and (iii) strategic improvements targeting key leverage factors can significantly enhance the composite sustainability index. Primary data were collected through field observations, interviews, and surveys, supplemented by secondary policy and spatial data. Results show an overall sustainability score of 51.84%, categorized as “sustainable”. Ecological (66.71%), social (60.71%), and infrastructural–technological (60.50%) aspects were sustainable, while economic (26.14%) and legal–institutional (45.14%) aspects were less sustainable. Key leverage factors influencing sustainability include microclimate regulation, canopy density, biodiversity, tourism management, consumer dependence on agroforestry products, product quality standardization, availability of processing industries, and the presence of management institutions and SOPs. Scenario analysis demonstrates that targeted improvements in these levers can substantially increase sustainability scores, with optimistic scenarios raising the aggregate index to 78.45%. Strengthening economic value chains, regulatory frameworks, management institutions, and data infrastructure is essential to enhance the adaptive capacity and long-term viability of urban agroforestry in Bogor City. Full article

This study examines the potential of digital twin (DT) technology to strengthen urban security, with a specific focus on flood risk management in smart cities. A DT is understood as a virtual representation of real-world assets and processes, continuously synchronised with data from the physical environment. Building on an analysis of the existing DT literature and maturity assessment, identified operational requirements and the authors’ expertise in crisis management, this study proposes a structured set of DT maturity levels with stage boundary conditions and illustrative measurable indications and designs a maturity-driven data content model for a flood-oriented DT. The framework identifies essential data layers, sensing requirements and integration mechanisms necessary for representing hydrological, infrastructural and environmental conditions at operationally meaningful update frequencies. This study further outlines the conceptual architecture of a flood DT and discusses its potential to support prediction, situational awareness and decision making across crisis management phases. By providing recommendations for DT implementation and highlighting opportunities for future development, this study contributes to ongoing efforts to enhance the resilience and safety of urban areas through advanced digital technologies. Full article

With the increasing density of urban underground space development, the soil disturbance induced by large-section rectangular pipe jacking poses a significant threat to the safety of underlying subway tunnels. Taking the Lihe Road utility tunnel project in Wuhan, which crosses over Metro Line 4, as the engineering background, a three-dimensional finite element (FE) model was established using Midas GTS NX to simulate the entire pipe jacking process. Field monitoring data from caisson excavation, ground improvement, pipe jacking, and backfill grouting were introduced for validation, enabling a systematic investigation of the influence mechanism of pipe jacking on existing tunnels. In the numerical simulation, the modified Mohr–Coulomb constitutive model was adopted for the soil, and a “portal-type” reinforcement system was introduced. The pipe jacking process was simulated equivalently with a 1.2 m advance per cycle. The results indicate that the ground settlement induced by pipe jacking exhibits a stage-wise accumulation pattern and eventually develops into a stable settlement trough. The vertical settlement of the tunnel follows an evolutionary law of “early occurrence in the near field, delayed response in the far field, and final convergence,” with peak settlements of 2.44 mm and 2.53 mm for the left and right lines, respectively. Ground improvement significantly mitigates soil deformation, reducing the maximum surface settlement from 45.5 mm to 11.1 mm, decreasing the tunnel’s peak vertical settlement by 37%, and reducing horizontal displacement by 64%, thereby effectively suppressing lateral soil extrusion. The proposed closed-loop analysis method of “numerical simulation–monitoring validation–measure evaluation” reveals the spatiotemporal evolution law of soil–tunnel interaction during pipe jacking construction and provides valuable reference for risk control in similar engineering projects. Full article

Eutrophication is a form of pollution caused by elevated nutrient concentrations in water bodies, leading to excessive algal growth and subsequent oxygen depletion. This process poses significant risks to aquatic ecosystems and overall water quality. This study investigates the spatial distribution of eutrophication in the Almyros Stream, aiming to develop a rapid and high-resolution approach for identifying eutrophication patterns and selecting representative sampling sites. Almyros is an urban stream in the western Heraklion Basin (Crete, Greece) that is subjected to considerable pressures from agricultural, industrial, urban, and tourism-related activities. Data for this study were collected using a drone equipped with a multispectral sensor. The multispectral bands, together with remote sensing indices associated with chlorophyll presence, served as input data. Chlorophyll presence is a key indicator of phytoplankton biomass and is widely used as a proxy for nutrient enrichment and eutrophication intensity in aquatic ecosystems. The k-means clustering algorithm was then applied to classify the data and reveal the eutrophication spatial patterns of the study area. The results show that the methodology successfully identified spatial variations in eutrophication-related conditions and generated robust eutrophication pattern maps. These findings underscore the potential of integrating remote sensing and machine learning techniques for efficient monitoring and management of water bodies. Full article

Urban flood information enables managers to rapidly synthesize comprehensive flood event profiles, serving as critical evidence for flood control decision making. Compared with traditional methods, public data offer unprecedented spatiotemporal granularity due to its high volume, multidimensionality, and real-time nature. In this paper, we investigated public data’s usefulness and generalizability of spatial feature differences using multi-source social media data as an entry point. We selected rainstorm events that occurred in three cities located in the North China Plain, the Southeast Coastal Region, and the Western Region of China, with vastly different developmental statuses in 2023. Then, multi-platform data from the events were collected and analyzed through crawling and topic mining. The results indicate that: (1) social media data from different sources are complementary to each other and can collectively extract plenty of neglected waterlogging points to supplement official data, with a supplementary rate reaching 171% on average; and (2) social media data has significant value in spatial characterization, which means that its availability remains constant despite geographical differences and can self-adapt to local geography, inhabitant profiles and social development levels. To address the issues of limited available data and essential information lacking during the analysis process, we propose recommendations for data processing and city managers to enhance the scientific value of social media data utilized in practice. Full article

The rapid deterioration of building facades presents substantial safety hazards in urban environments, necessitating advanced, automated inspection solutions. While computer vision (CV) and deep learning (DL) techniques have shown promise for defect analysis, critical gaps remain in achieving real-time, quantitative, and generalizable damage assessment suitable for robotic deployment. Current methods often lack precise metric quantification, struggle with diverse material appearances, and are computationally intensive for on-site processing. To address these limitations, this paper introduces a fully automated, end-to-end inspection framework integrating a wall-climbing robot, a real-time vision-based analysis system, and a digital twin management platform. The primary contributions are threefold: (1) a novel, fully integrated robotic framework for autonomous navigation, multi-sensor data collection, and real-time analysis; (2) a lightweight, synthetic data-augmented DL model for real-time defect segmentation and metric quantification, achieving a mean Average Precision (mAP) of 0.775 for segmentation, an average defect length error of 1.140 cm, and an average center position error of 0.826 cm; (3) a cloud-based digital twin platform enabling quantitative defect visualization, spatiotemporal traceability, and data-driven project management, with the on-site inspection cycle demonstrating a responsive latency of 2.8–4.8 s. Validated through laboratory tests and real building projects, the framework demonstrates significant improvements in inspection efficiency, quantitative accuracy, and decision support over conventional methods. Full article

Following the launch of the Sustainable Development Goals (SDGs) process at the Rio+20 Summit, China has progressively strengthened sustainability-oriented considerations in development target setting and administration cadre performance assessment, which provides an institutional window to examine how the transformation of development goals reshapes urban land supply patterns. This study develops a spatial general equilibrium model and uses panel data for 286 prefecture-level cities in China from 2007 to 2021 to examine how the transformation of development goals affects urban land supply patterns. The results show that higher economic growth targets significantly expand total land supply, raise the ratio of industrial to residential land supply, and tighten floor-area-ratio (FAR) regulation. “Soft constraint” wording dampens the effect on land supply scale but strengthens the effects on land supply structure and FAR regulation, while the degree of vertical and horizontal target escalation generates substantial heterogeneity in these relationships. Moreover, after governance shifted from growth-oriented to sustainability-oriented objectives, the marginal effectiveness of using land supply structure and FAR regulation to deliver predetermined growth targets declined significantly. This study provides empirical evidence and policy-relevant insights for improving sustainability-oriented target accountability systems and urban governance incentive mechanisms. Full article