Search Results (119)

In this study, we investigate the spatial evolution characteristics and driving mechanisms of the Barkhor Historic Area in Lhasa, Tibet, China, in the context of rapid urbanization and heritage conservation. Using multi-temporal spatial data, an integrated analytical framework combining a geographical information system, spatial design network analysis, and GeoDetector software 2015 is employed to examine land use, road network structure, and building morphology. The results show that the overall spatial structure remains highly continuous within a stable pilgrimage-based spatial framework, with spatial evolution occurring primarily through functional reorganization and incremental adjustment within the existing structure. Land use shifts from relatively single functions to mixed patterns, with commercial and public services increasingly concentrated along pilgrimage routes. The road network maintains a stable structural backbone centered on the pilgrimage system, while building morphology evolves through small-scale infill and localized transformation, preserving traditional spatial scales. Driving factor analysis reveals a transition from single-factor dominance to multi-factor coupling. Socio-economic factors dominate early-stage changes, spatial structure provides a persistent organizational framework, and cultural heritage increasingly shapes spatial continuity and functional adaptation. This study highlights a form of pilgrimage-oriented spatial adaptation in religious–cultural historic areas, characterized by structural continuity, functional embedding, and multi-factor coupling, and provides new perspectives for adaptive conservation and spatial governance in historic urban areas. Full article

Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. Full article

The paper introduces a computationally efficient semantic-aware route planning framework for indoor mobile robots, designed for real-time execution on resource-constrained edge hardware (Raspberry Pi 5, CPU-only). The proposed architecture fuses monocular object detection with 2D LiDAR-based range estimation and integrates the resulting semantic annotations into the Nav2 Route Server for penalty-weighted route selection. Object localization in the map frame is achieved through the Angular Sector Fusion (ASF) pipeline, a deterministic geometric method requiring no parameter tuning. The ASF projects YOLO bounding boxes onto LiDAR angular sectors and estimates the object range using a 25th-percentile distance statistic, providing robustness to sparse returns and partial occlusions. All intrinsic and extrinsic sensor parameters are resolved at runtime via ROS 2 topic introspection and the URDF transform tree, enabling platform-agnostic deployment. Detected entities are classified according to mobility semantics (dynamic, static, and minor) and persistently encoded in a GeoJSON-based semantic map, with these annotations subsequently propagated to navigation graph edges as additive penalties and velocity constraints. Route computation is performed by the Nav2 Route Server through the minimization of a composite cost functional combining geometric path length with semantic penalties. A reactive replanning module monitors semantic cost updates during execution and triggers route invalidation and re-computation when threshold violations occur. Experimental evaluation over 115 navigation segments (legs) on three heterogeneous robotic platforms (two single-board RPi5 configurations and one dual-board setup with inference offloading) yielded an overall success rate of 97% (baseline: 100%, adaptive: 94%), with 42 replanning events observed in 57% of adaptive trials. Navigation time distributions exhibited statistically significant departures from normality (Shapiro–Wilk, p < 0.005). While central tendency differences between the baseline and adaptive modes were not significant (Mann–Whitney U, p = 0.157), the adaptive planner reduced temporal variance substantially (σ = 11.0 s vs. 31.1 s; Levene’s test W = 3.14, p = 0.082), primarily by mitigating AMCL recovery-induced outliers. On-device YOLO26n inference, executed via the NCNN backend, achieved 5.5 ± 0.7 FPS (167 ± 21 ms latency), and distributed inference reduced the average system CPU load from 85% to 48%. The study further reports deployment-level observations relevant to the Nav2 ecosystem, including GeoJSON metadata persistence constraints, graph discontinuity (“path-gap”) artifacts, and practical Route Server configuration patterns for semantic cost integration. Full article

The aspiring United Nations Educational, Scientific and Cultural Organization (UNESCO) Global Geopark of Chefchaouen includes part of the Talassemtane National Park (TNP), classified by UNESCO as an exceptional natural heritage site within the Intercontinental Mediterranean Biosphere Reserve (RBIM). The other section corresponds to the Ghomara Coast (GC), characterized by an outstanding succession of metamorphic rocks. This study identifies and highlights the most significant sites of geological interest (geosites and geodiversity sites) in the territory. Forty-two sites are proposed as geological heritage sites, thirty of which are organized into four accessible georoutes (Oued Laou Valley, Ghomara Coast, Talambote–Akchour, and Chaouen–Ametrasse), while the other twelve are located along trails and forest tracks inside or near the TNP. These sites cover a wide range of geological typologies, including structural geology, stratigraphy–sedimentology, paleontology, geomaterials, petrology, geomorphology, and hydrogeology. To classify and rank the sites objectively, a numerical methodology based on the recent literature was applied. Scientific value (SV), Potential Educational Use (PEU), and Potential Touristic Use (PTU) were quantified using multiple criteria, facilitating route selection according to user needs. Degradation Risk (DR) was also measured, providing managers with essential guidance for an appropriate geoconservation plan. Actions consistent with UNESCO Global Geoparks Network criteria are proposed to improve conservation, support education, and promote sustainable tourism, thereby enhancing economic activity in the region. The initiative aims to promote the region’s exceptional geological, cultural, and natural heritage. The Chefchaouen Geopark was designated a deferred candidate during the UNESCO Global Geoparks Council meeting of 8–9 September 2024. According to Section 5.5 of its guidelines, the Council may defer an application for up to two years to allow improvements without requiring a second field evaluation. To consolidate the Chefchaouen candidacy, we developed a strategy to strengthen compliance with UNESCO requirements, reduce the risk of final rejection, and maintain the territory’s credibility with international networks and partners. This work presents an operational, costed, and scheduled roadmap enabling stakeholders at all levels to converge toward a complete and coherent application. Full article

The emergence of Generative Artificial Intelligence (GenAI), capable of interpreting and reasoning with human language, has catalyzed a paradigm shift across various societal sectors. Within the tourism industry, GenAI is increasingly utilized to facilitate personalized itinerary planning, destination recommendations, and the provision of optimal route information. This study evaluates the reliability of GenAI in identifying the nearest metro station within a walking distance from tourist attractions in Busan, South Korea. Furthermore, it aims to empirically verify the determinants influencing the correctness of AI-generated responses compared to network-based shortest-path analyses. The empirical results demonstrate that Google’s Gemini 3 Pro model achieved superior performance, recording an accuracy rate of 65.0%. Regression analysis revealed that for both Gemini and GPT models, the volume of news articles associated with an attraction—representing media visibility—significantly increased the likelihood of accurate information provision. Notably, the Gemini model exhibited distinct sensitivity to geographic factors and text similarity metrics, suggesting a difference in how it processes spatial context compared to other models. Consequently, this study underscores the importance of high-quality AI-generated tourism data and offers significant contributions to the advancement of sophisticated personalized travel planning systems and GeoAI research focused on spatial problem-solving. Full article

Integrating Geostationary (GEO), Medium Earth Orbit (MEO), and Low Earth Orbit (LEO) satellite systems offers a promising solution for enhancing communication resilience in disaster-prone island regions. However, effective integration via Software-Defined Wide Area Networks (SD-WANs) faces challenges due to the heterogeneous stochastic characteristics of these links. This study presents a comprehensive performance benchmark of GEO, MEO, and LEO satellite links based on long-duration empirical campaigns conducted in Taiwan. Our findings quantify critical integration hurdles, specifically the “long-tail” latency distribution in LEO links induced by frequent handovers and significant TCP throughput degradation modeled by the Mathis equation. Furthermore, empirical tests demonstrate that simplistic link aggregation across these heterogeneous orbits results in severe packet reordering and goodput collapse. Based on these results, we propose a conceptual resilience-oriented SD-WAN architecture incorporating intelligent failover thresholds and application-aware routing policies. This work provides foundational data and a design framework to guide the future development of robust multi-layered satellite communication systems for disaster management. Full article

Skyros Island, the largest island of the Sporades Complex (NW Aegean Sea, Greece), preserves a geologically diverse record spanning from the Upper Permian to the Quaternary, including crystalline and non-metamorphosed carbonate rocks, ophiolitic rocks and mélanges, medium-grade metamorphic units, rare Miocene volcanic rocks, and impressive fossil-bearing sediments and tufa deposits, together with historically significant quarry and mining landscapes. Through a comprehensive evaluation of the geological heritage of Skyros, this study proposes a transferable, results-based framework for geoconservation, geoeducation, and tourism space management within a geopark context. A systematic inventory of twenty (20) geosites, including six (6) flagship case studies, was established based on scientific value, dominant geodiversity type, risk of degradation, accessibility, educational and tourism potential. The assessment integrates the Scientific Value and Risk of Degradation criteria with complementary management and sustainability indicators. The results demonstrate consistently high scientific value across the selected geosites, with several reaching maximum or near-maximum scores due to their rarity, integrity, and reference character at a regional to international scale. Although some geosites exhibit elevated degradation risk, overall vulnerability is considered manageable through targeted conservation measures and spatially explicit visitor management. Based on the assessment results, a network of thematic georoutes was developed and evaluated using route-level indicators, including number of geosites, route length, educational potential, tourism suitability, accessibility, and contribution to responsible geotourism. The study demonstrates how integrated geosite and georoute assessment can support sustainable land management and confirms that Skyros Island meets key criteria for inclusion in the Hellenic Geoparks Network, providing a robust scientific basis for future UNESCO Global Geopark designation. Full article

This paper examines the application of Artificial Intelligence (AI) to protect satellite communication networks, focusing on the identification and prevention of cyber threats. With the rapid development of the commercial space sector, the importance of effective cyber defense has grown due to the increasing dependence of global infrastructure on satellite technologies. The study applies a structured comparative analysis of AI methods across three main satellite architectures: geostationary (GEO), low Earth orbit (LEO), and hybrid systems. The methodology is based on guiding research question and evaluates representative AI algorithms in the context of specific threat scenarios, including jamming, spoofing, DDoS attacks, and signal interception. Real-world cases such as the KA-SAT AcidRain attack and reported Starlink jamming in Ukraine, as well as experimental demonstrations of RL-based anti-jamming and GNN/DQN routing, are used to provide evidence of practical applicability. The results highlight both the potential and limitations of AI solutions, showing measurable improvements in detection accuracy, throughput, latency reduction, and resilience under interference. Architectural approaches for integrating AI into satellite security are presented, and their effectiveness, trade-offs, and deployment feasibility are discussed. Full article

With the rapid development of geological blockchains and Internet of Things-based data acquisition technologies, massive amounts of heterogeneous data are constantly emerging. However, this data is stored in a distributed manner across different organizational or business blockchains. Data sharing among multiple geological blockchains faces numerous challenges, either exposing sensitive data during verification or lacking effective authorization mechanisms. Therefore, how to achieve fine-grained access control and privacy protection across multiple blockchains has become a critical issue that must be addressed in geological data sharing. In this paper, we propose GeoCross, a cross-chain geological data sharing framework that enables fine-grained authorization management and privacy protection. First, GeoCross provides a hierarchical hybrid encryption mechanism that uses symmetric encryption for geological data protection and ciphertext-policy attribute-based encryption to enable flexible cross-chain access policies. Second, we integrate a Groth16-based zero-knowledge proof mechanism, which allows a chain to verify the existence, integrity, and accessibility of off-chain data without revealing the content. Furthermore, we introduce a Reputation-based Non-interactive Relay node Selection protocol (RNRS), which enhances the trustworthiness and fairness of cross-chain routing. Finally, we implement GeoCross in a multi-chain Hyperledger Fabric environment and evaluate its performance under real-world workloads. Results show that Groth16 verification requires only three bilinear pairings, achieving a throughput of up to 390 tps on a single chain and 1550 tps in a concurrent multi-chain environment. Even with 50% malicious nodes, the RNRS protocol still maintains a success rate of over 91%. These results demonstrate that GeoCross provides an efficient and practical solution for secure and privacy-preserving cross-chain geological data sharing. Full article

Traditional route optimization frameworks often suffer from “spatial blindness,” addressing the problem through abstract matrices devoid of geographical context. To address this fundamental methodological gap, this study proposes the Iterative Score Propagation Algorithm (ISPA), a transparent, GNN-inspired framework that reframes optimization as a holistic corridor problem. ISPA’s robustness and superiority were tested against established Multi-Criteria Decision-Making (MCDM) methods (WLC, TOPSIS, VIKOR) across three diverse engineering scenarios (Rural Highway, Pipeline, Trekking Trail) and two distinct weighting philosophies (Entropy and AHP). The holistic analysis reveals that ISPA achieves the highest final score (0.815) across all six test conditions, demonstrating both the highest overall mean performance (0.629) and the greatest stability (1.000). Furthermore, its flexible cost function successfully modeled unconventional objectives, such as a “climbing reward,” enabling a paradigm shift from cost minimization to experience maximization. ISPA’s superior performance stems from its structural advantage in contextualizing spatial data. This work introduces a new, spatially-aware approach that transforms route planning from a static calculation into a dynamic design and scenario analysis tool for planners and engineers. Full article

Cities are major sources of anthropogenic carbon dioxide (CO₂) emissions, making the study of intra-urban CO₂ concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city scale. To address these challenges, an analysis supported by multi-source data and GeoAI methods is carried out to examine the spatial distribution, vertical variation, temporal dynamics, and driving factors of CO₂ concentrations in urban areas. We combined OCO-2 satellite-derived XCO₂ data (2014–2024) with ground-based measurements from the Shanghai Tower (August 2024 to March 2025), alongside meteorological and socioeconomic variables. The analysis employed spatial interpolation (inverse distance weighting), nonparametric testing (Mann–Whitney U test), time series decomposition, ordinary least squares (OLS) regression, and machine learning techniques including random forest and SHAP (SHapley Additive exPlanations) analysis. Results reveal that CO₂ concentrations are significantly higher in central urban districts compared to suburban areas, with notable spatial heterogeneity. Elevated levels were detected near ports and ferry routes, with airports and industrial emissions identified as principal contributors. Vertically, CO₂ concentrations decline with increasing altitude but exhibit a peak at mid-level heights. Temporally, a pronounced seasonal pattern was observed, characterized by higher concentrations in winter and lower levels in summer. Both OLS regression and machine learning models highlight proximity to emission sources, wind speed, and temperature as key determinants of spatial CO₂ variability, with these factors collectively explaining 67% of the variance in OLS models. This study demonstrates how multi-source data and advanced methods can capture the spatial, vertical, and seasonal dynamics and driving factors of urban CO₂ concentrations, offering insights for policy, planning, and mitigation. Full article

The contemporary landscape is characterised by overlapping values and pressures, where ecosystem services and cultural spaces are used by diverse categories of users. In fragile contexts such as the Phlegraean Fields in Italy, the exponential growth of mass tourism has intensified the anthropogenic impacts, exacerbated by limited landscape awareness among local communities. Thus, walkability fosters direct exploration, while experiential transects provide a lens to read ecological, cultural, and perceptual layers of places. Together with digital storytelling, these approaches converge in a phygital approach that enriches physical experience without supplanting it. The study covered approximately 115 km of routes across five municipalities, combining road audits, an 11-item survey, participatory mapping, and ArcGIS StoryMaps. Results showed a structurally complex and functionally fragile mobility system: sidewalks are discontinuous, lighting insufficient, less than one quarter of the network is fully pedestrian, and cycling facilities are almost absent. At the same time, digital layers diversified routes and supported situated learning. By integrating geo-spatial analysis and phygital tools, the research demonstrates a replicable strategy to enhance the awareness and sustainable enjoyment of complex landscapes. The present research is part of the PNRR project Changes ‘PE5Changes_Spoke1-WP4-Historical Landscapes Traditions and Cultural Identities’. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

The lane graph is critical for applications such as autonomous driving and lane-level route planning. While previous research has focused on extracting lane-level graphs from aerial imagery using convolutional neural networks (CNNs) followed by post-processing segmentation-to-graph algorithms, these methods often face challenges in producing sharp and complete segmentation masks. Challenges such as occlusions, variations in lighting, and changes in road texture can lead to incomplete and inaccurate lane masks, resulting in poor-quality lane graphs. To address these challenges, we propose a novel approach that refines the lane masks, output by a CNN, using diffusion models. Experimental results on a publicly available dataset demonstrate that our method outperforms existing methods based solely on CNNs or diffusion models, particularly in terms of graph connectivity. Our lane mask refinement approach enhances the quality of the extracted lane graph, yielding gains of approximately 1.5% in GEO F1 and 3.5% in TOPO F1 scores over the best-performing CNN-based method, and improvements of 28% and 34%, respectively, compared to a prior diffusion-based approach. Both GEO F1 and TOPO F1 scores are critical metrics for evaluating lane graph quality. Additionally, ablation studies are conducted to evaluate the individual components of our approach, providing insights into their respective contributions and effectiveness. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article