Search Results (572)

Climate risk assessments for critical infrastructure are essential to identifying and predicting vulnerabilities early in the asset life cycle, enabling proactive mitigation through the implementation of technical and nature-based solutions (NbS) before impacts occur. However, such assessments often rely on dense quantitative indices that are difficult for non-technical stakeholders to interpret. To address this challenge, this paper presents an open-source decision support platform that combines OpenStreetMap site characterization, qualitative pre-screening, a quantitative IPCC AR6-aligned risk chain, and a downstream NbS recommendation layer. The approach deploys Large Language Models (LLMs) to translate analytical outputs into accessible narrative explanations. End-to-end site-characterization processing across three European demonstration sites took between 29 and 70 s. An exploratory ablation study investigated the faithfulness of the AI-generated explanations using three complementary metrics, demonstrating that the generated hazard assessments remained factually grounded and free from fabricated numerical values. Introducing example reports (exemplars) into the prompt context further stabilized the reliability of the output for complex risk indicators. Finally, a small blind expert evaluation with six researchers from adjacent technical domains provided convergent evidence: five of six raters independently rated with-exemplar Hazard Reports higher on completeness; among the five raters who expressed a directional preference, all five favored the with-exemplar condition (sign test, p = 0.031). Furthermore, seven of eight aggregate dimension-level comparisons confirmed that with-exemplar reports scored at least as high as their ablated counterparts. Full article

The Population-based Cancer Registry of the Municipality of Pasto (RPCMP) faces significant challenges in spatial data integrity due to the high variability, inconsistency, and ambiguity of manually collected addresses. Such limitations restrict the analytical capacity of the YACHAY-GIS platform to generate precise geospatial intelligence across multiscalar levels. To address these issues, this paper proposes a novel hybrid address normalization framework structured in two sequential stages. First, a specialized deterministic geocoding engine, optimized for local urban morphology, is utilized. Second, for highly non-standard records—characterized by “block and lot” (manzana y lote) nomenclatures—a workflow based on Large Language Models (LLMs) is integrated. To ensure computational efficiency in resource-constrained environments, a Supervised Fine-Tuning (SFT) process was implemented using the Low-Rank Adaptation (LoRA) technique across various open-source LLMs. Experimental results demonstrate that the workflow integrated with the Llama-3.2-3B-Instruct model achieved the highest performance, with a normalization accuracy of 99.40%. The end-to-end evaluation shows an increase in spatial recovery from a 33.02% baseline to 55.13%, effectively isolating the semantic gap from the underlying cartographic limitations of the region. This architecture provides a scalable data engineering model for oncological registries, with future work focused on integrating external spatial providers such as OpenStreetMap and Colombia’s Agustín Codazzi Geographic Institute (IGAC) to further enhance geographic coverage. Full article

Oil extraction and agricultural production are central to North Dakota’s economy, yet their spatial interactions remain poorly understood. This study conducts a statewide geospatial analysis integrating OpenStreetMap data, GIS processing, DBSCAN clustering, and spatial statistics to examine the colocation of 7102 oil well and 4277 grain silo sites. Hotspot and spatial heterogeneity tests using the Getis–Ord Gi* statistic and local Moran’s I reveal a pronounced spatial divide: oil activity is tightly clustered in the western Bakken region, whereas grain storage facilities concentrate across central and eastern counties. The limited geographic overlap suggests minimal systemic land-use conflict, though localized high-intensity interactions emerge in McKenzie, Dunn, and Mountrail counties. These patterns provide stakeholders with insight into potential shared logistics pressures and localized land-use tensions. More broadly, the study demonstrates the value of spatial data mining techniques applied to free, publicly available data for identifying intersectoral industrial patterns that inform policy and infrastructure planning across North Dakota. Full article

Traditional urban morphological analyses are structurally limited in terms of both systemic diversity and empirical scale due to reliance on manual methods. In contrast, decoding the complex fabric of rapidly growing cities necessitates data-driven, scalable approaches. To address this gap, this study proposes a multi-scale pipeline that classifies urban form systematically and reproducibly from open spatial data, and applies it comparatively to three Anatolian cities of contrasting typo-morphological character: Elazığ, Erzincan and Mardin. From street networks and building geometries, an integrated morphometric matrix was assembled by computing network topology and orientation metrics, space syntax configurational accessibility, morphological tessellation, coverage area ratio, building form–volume indicators, neighbourhood and adjacency measures, and Local indicators of spatial association (LISA) spatial autocorrelation. After transformation and standardisation, urban typologies were derived through Principal Component Analysis and grouped with spatially weighted k-means (Geo-KMeans). Three findings stand out. First, the cities trace a distinct morphological spectrum that runs from Mardin’s organic historical fabric to Erzincan’s relatively planned grid structure, with the radial polarised Elazığ occupying an intermediate, transitional position between the two. Second, accessibility and built density prove only weakly related (r = 0.05–0.25). Third, six, five and four morphological typologies emerged, triangulated against LISA hot spot clusters and space syntax maps. Overall, this reproducible framework offers planners a systematic, data-driven basis for exploratory morphological assessment rather than a definitive, universal typology. Full article

Urban heat and walkability are often assessed separately, although pedestrians experience street connectivity, topography, vegetation, and thermal exposure simultaneously. This study develops a reproducible open-data workflow to examine the spatial relationship between walkability potential and surface thermal pressure in Loja, Ecuador, an intermediate Andean city shaped by valley morphology, steep slopes, uneven urban expansion, and heterogeneous green-space distribution. The analysis combines Landsat-derived land surface temperature, OpenStreetMap urban-form indicators, DEM-derived slope, composite spatial indicators, and spatial autocorrelation across 100 m, 250 m, and 500 m grids. The results show that walkability potential, observed LST-based heat intensity, and walkability–heat balance is spatially structured rather than randomly distributed. At the 250 m scale, heat-intensity clusters were spatially selective, indicating that surface thermal pressure is concentrated in specific parts of the retained analytical grid rather than uniformly distributed across the city. The 250 m grid provided the most interpretable balance between local detail and spatial stability for this case study, while the 100 m and 500 m grids revealed the sensitivity of the results to spatial aggregation. The study does not measure physiological thermal comfort or pedestrian heat stress. Instead, it offers an exploratory diagnostic framework for identifying where pedestrian-supportive urban form and surface thermal pressure overlap or diverge. This approach can help data-constrained intermediate cities prioritize areas for field verification, shade assessment, green-infrastructure planning, and more detailed pedestrian-level thermal studies. Full article

Complete neighbourhoods, places where residents can meet their daily needs on foot, have become a central component of healthy and sustainable urban planning. Yet most assessment frameworks are calibrated for dense metropolitan environments, leaving rural and peri-urban municipalities without operational tools suited to their territorial needs. This article presents MilieuxVie, an open-source, browser-based interactive mapping application developed for the Laurentides health region of Québec (76 municipalities, 11 land-based unorganised territories, 2 indigenous territories and 4 aquatic administrative units; 93 territorial units in total; ~680,000 inhabitants). The tool evaluates the spatial accessibility of 12 service categories drawn from the Vivre en Ville (2026) complete-neighbourhood framework and OpenStreetMap data, using 2026 residential parcels from the provincial property assessment roll as origin points and weighting results by number of dwelling units. Three adaptive radius tiers (dense, intermediate, rural), based on residential dwelling-unit density (dwellings per km² of residentially designated urban land), scale the distance standards to settlement density. Because thresholds are scaled to settlement density, scores express context-relative service proximity rather than a uniform pedestrian standard and should not be read as directly comparable absolute accessibility across rural, peri-urban, and urban settings. A dedicated urban perimeter mode further disaggregates analysis to sub-municipal built-up zones, aligning the tool with Québec’s provincial Government land-use planning guidelines (GLPG). Gap analysis outputs identify which service types fall below the 70% coverage target, helping elected officials and planners identify where to focus further analysis. Results illustrate the scope of accessibility deficits across the region and highlight the analytical limits of uniform distance thresholds when applied beyond metropolitan contexts. Scores differ significantly across different settings (Kruskal–Wallis p = 0.006); the adaptive radius tiers narrow but do not close the structural gap, with rural municipalities scoring significantly lower than dense ones. The tool is freely available and requires no software installation, making it directly deployable by local planning offices. Full article

Urban multi-UAV navigation in dense building environments requires not only collision-free geometric paths but also executable flight processes under motion constraints and inter-UAV safety requirements. A static path that is feasible in a geometric map may still fail during closed-loop execution because of velocity limits, acceleration constraints, local path-association errors, and coupled multi-UAV interactions. Meanwhile, end-to-end reinforcement learning often suffers from unstable training, weak geometric interpretability, poor early-stage safety, and high sample complexity. To address these issues, this paper proposes a hierarchical planning-and-learning framework that connects static reference path generation, executable reference tracking, successful demonstration distillation, and conflict-aware residual reinforcement learning. First, three-dimensional reference paths are generated offline in an OpenStreetMap-based urban scene represented by cuboid buildings. Second, a damped reference-tracking mechanism transforms these static paths into closed-loop executable reference processes through local path association, monotonic progress updating, path recapture, look-ahead guidance, and bounded action construction. Third, successful pure-reference executions are distilled for behavior-cloning initialization. Finally, a bounded residual TD3 module is introduced as a local conflict-correction mechanism around the verified executable reference baseline. Experiments in an urban scene containing 754 buildings show that simplified tracking strategies fail to execute the static paths reliably, whereas the proposed full-damped reference-tracking controller achieves a 91.67% all-success rate and eliminates building collision episodes in the tracking-ablation test. Speed-sensitivity experiments at 10, 15, and 20 m/s show the same 91.67% all-success rate, indicating that the conclusion is not dependent on a single speed setting. In constructed conflict-stress tests, the conflict-aware residual TD3 module increases the all-success rate from 33.33% to 80.09%, reduces inter-UAV collision episodes from 66.67% to 11.57%, and improves the hard-safety satisfaction rate from 33.33% to 87.04%. These results show that the main contribution of the proposed framework lies in converting static geometric paths into executable reference trajectories and further enabling bounded residual correction under inter-UAV conflict conditions. Full article

Remote sensing vision–language models, such as RemoteCLIP and GeoRSCLIP, have advanced image–text representation learning. However, they rely on manually curated caption datasets that are expensive to scale and provide only global image-level supervision. In this paper, we introduce OSM-CLIP, a framework that exploits the freely available, continuously growing annotations of OpenStreetMap (OSM) to provide regionally scalable, patch-level supervision for remote sensing image-text learning. We construct a large-scale dataset of over 265,000 satellite images covering the contiguous United States, each automatically paired with fine-grained geographic annotations scraped from OSM and mapped to individual image patches. A contrastive loss operating at the patch level associates each image region with its corresponding OSM textual description, enabling the model to learn spatially grounded representations without any manual labeling effort. After fine-tuning on standard remote sensing captioning datasets, OSM-CLIP achieves an average improvement of 10.81% in zero-shot classification, 5.06% in text-to-image retrieval (R@1), and 3.87% in image-to-text retrieval (R@1) over existing methods across 13 classification and 4 retrieval benchmarks. Our results demonstrate that freely available geographic annotations can serve as a powerful source of supervision for remote sensing vision–language models in regions with high-quality OSM coverage. Full article

Accurate fleet electrification assessment requires microscopic traffic simulation grounded in real-world demand, physics-based vehicle models, and routing that respects the lane-connection topology of urban networks. We present ISTVEL (Istanbul Simulation Tool for Vehicle Electrification), an open-source framework that ingests hourly Istanbul Metropolitan Municipality (IMM) loop-detector data, snaps detectors to OpenStreetMap edges, synthesises SUMO demand via a connection-graph Breadth-First Search (BFS) algorithm eliminating teleportation artifacts, and post-processes tripinfo.xml output to compute per-trip energy, use-phase CO₂, and energy operating cost (ECO₁₀₀), correctly distinguishing gross battery draw, regenerative recovery, and net grid consumption. Applied to the Kadıköy district of Istanbul ($3.2{\mathrm{km}}^2$, 08:00–09:00, January 2025, $\approx 2950$ vehicles), ISTVEL demonstrates that a full battery-electric vehicle (BEV) fleet reduces use-phase (operational) CO₂ by 80.1% and energy operating cost by 66.5% versus the internal-combustion-engine vehicle (ICEV) baseline at current Turkish grid intensity ($\gamma =0.45\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$). However, these figures reflect use-phase emissions only (tailpipe combustion for ICEV; upstream grid emissions $\gamma \times E_{\mathrm{net}}$ for BEV) and exclude vehicle manufacturing, battery production, and upstream fuel extraction. Opportunistic in-transit dynamic wireless power transfer (DWPT) charging at 0.5 km spacing reduces post-trip battery replenishment demand by a further 67.1%, shifting grid supply from post-trip charging to in-transit delivery; total system electricity demand (including DWPT supply) is 895.7 kWh, marginally above the plain-BEV baseline of 848.1 kWh due to charging losses at ${\eta }_{\mathrm{cs}}=0.95$. Framework transferability is further demonstrated on the Fatih district under an identical protocol. Full article

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides a crowdsourced solution for geospatial data collection, it is commonly hindered by significant heterogeneity—manifested in inconsistent data completeness, positional inaccuracies and poor topological consistency across different datasets. To address these critical limitations, this study proposes an intelligent geospatial agent framework designed to autonomously fuse building data from multiple heterogeneous sources, including VGI, Very High-Resolution (VHR) satellite imagery, and Light Detection and Ranging (LiDAR) data. This study’s core innovative points are embodied in three key modules: a supervised VGI quality verification module that leverages the Random Forest model to evaluate the reliability of individual building feature elements; a hybrid building extraction engine which integrates LiDAR data with the Segment Anything Model (SAM) to realize zero-shot building extraction; and a cognitive rule engine that adopts Multi-Criteria Decision Analysis (MCDA) for the intelligent resolution of spatial conflicts. Comprehensive validation experiments were conducted in two African cities experiencing rapid urbanization—Kigali and Dar es Salaam. The results show that the proposed framework boosts data completeness by more than 29% and attains a fused dataset F1-Score of 0.919, effectively converting incomplete VGI data into a geospatial resource with near-official authoritative quality. Full article

Thailand’s Provincial Electricity Authority (PEA) is rolling out Advanced Metering Infrastructure (AMI) under its smart-grid initiative, requiring a reliable last-mile wireless network across heterogeneous propagation environments. Narrowband IoT (NB-IoT) is a leading candidate, but per-area deployment decisions have lacked a data-driven framework anchored to measured Thai propagation. Building on our sixteen-site composite-channel characterisation, this study presents a machine-learning feasibility-prediction framework integrating measured channel parameters (n, σ_sh, $\widehat{m}$), an OpenStreetMap-derived synthetic meter-density layer, and a benchmark of Random Forest, Gradient Boosting (GB), and Multi-Layer Perceptron classifiers trained on Monte-Carlo coverage labels to predict 95% RSRP-coverage feasibility per spatial cell. Across 411 cells from four Thai sites spanning Urban Dense, Urban Outdoor, Suburban, and Rural environments, GB achieves accuracy 0.971 and F1 0.969 at 1.7 ms inference latency—four orders of magnitude faster than direct Monte-Carlo simulation. The ML predictor approximates the Monte-Carlo engine under the assumed composite-channel model. A theoretical LPWAN comparison places NB-IoT as recommended for Suburban and Rural AMI; Suphan Buri (Rural) is the only RECOMMENDED case (88.5% cells feasible), with hybrid PLC backhaul suggested for dense urban areas. Full article

Preventing large-scale illegal migration is one of the EU’s highest priorities. In this study, we analyze the potential for integrating and fusing remote sensor data with a wider range of data streams to enhance border security situational awareness, specifically targeting illegal migration. The aim was to develop a dynamic predictive risk analysis model to identify high-risk zones for illegal border crossings at Croatia’s external EU borders. The model’s methodological framework is based on the integration of Geographic Information Systems (GISs), Multi-Criteria Analysis (MCA), and the Analytic Hierarchy Process (AHP). The model utilizes various environmental and infrastructure variables derived from the open-source databases ESA WorldCover and OpenStreetMap to generate a categorized risk map showing areas of lowest, moderate, and highest risk for illegal border crossing. The model was quantitatively verified using a weighted detection-versus-background design against 7481 geocoded border crossing incidents, demonstrating high predictive skill and robust calibration (Continuous Boyce Index up to 0.97) when controlling for patrol effort bias and spatial autocorrelation. High-resolution historical satellite imagery showing activities related to illegal migration was used for the generation of labeled datasets for AI training. Features such as suspicious vans, river boats, tire tracks, tents, illegal campsites, and clusters of individuals were observed in high-resolution Airbus and Maxar historical satellite images. The model can be used for various practical applications, including the strategic allocation of surveillance resources and the enhancement of frontier and pre-frontier intelligence, enabling more informed actions and optimized operations. Full article

Urban Green Spaces (UGSs) monitoring usually focuses on the extraction of vegetation in the physical layer, while neglecting their functional attributes. This renders the monitoring results unable to objectively reflect the rationality of UGS planning. To address these issues, this study proposes a multi-level classification method integrating multi-source remote sensing and geospatial big data to bridge the semantic gap between the physical layer and the functional layer. In this method, a strategy of prior knowledge injection and semantic reconstruction was developed through the fine-tuning of a BERT model with cross-mapping rules. This strategy aims to classify the urban area into 24 functional categories, generating the social-functional basemap in a functional layer, based on Point of Interest (POI), OpenStreetMap (OSM), and Global Urban Boundary (GUB). Meanwhile, a novel deep learning architecture, namely the Multi-Shape and Spectral Aware Network (MSSANet), was designed for precise vegetation classification of UGSs in the physical layer. Finally, a “function-first, vegetation-second” coupling paradigm containing three functional attribute layers, referring to the Code for Classification of UGS in China (CJJ/T 85-2017), was established. This paradigm integrates the social-functional basemap with physical vegetation patches to build a multi-level UGS classification framework, i.e., the 5 major UGS categories, 11 intermediate UGS categories, and 24 fine-grained UGS sub-categories. Experiments conducted in Jinan and Qingdao, China, demonstrate the efficacy of the proposed method for refined multi-level UGS mapping. Full article

Despite growing policy support for active travel, the fatality rate of vulnerable road users has remained persistently high in recent years, while the emergence of autonomous vehicles has further increased the complexity of mixed traffic environments. Interactions between cyclists and motorized vehicles are a major contributor to these fatalities, highlighting the urgent need for effective cyclist protection strategies. As one of the most widely adopted active transport modes, cycling safety cannot be assessed solely through crash statistics; understanding cyclists’ perceived safety is equally critical, as it reflects how infrastructure design and dynamic traffic conditions influence cycling behavior. In this study, we propose a cyclist safety perception framework that combines vision–language models with interpretable machine learning to analyze perceived safety in mixed traffic scenarios. A vision–language model is employed to generate semantic descriptions of traffic scenes, while an Explainable Boosting Machine quantifies both individual and interactive contributions of traffic-related features. By integrating visual information with road attributes extracted from OpenStreetMap, the proposed framework achieves a binary safety classification accuracy of 71% and a mean absolute error of 1.01 on a safety score scale ranging from 1 to 9. The results demonstrate the potential of combining multimodal perception and explainable models to support cyclist-centered safety assessment and inform sustainable and intelligent transportation system design. More specifically, the results show that protected cycling infrastructure is the most significant factor in improving perceived safety, whereas road construction has the opposite effect. Full article

Building-integrated photovoltaics (BIPV) provide an important pathway for expanding distributed solar generation in dense urban areas, but comparable evidence on roof–facade resources across different urban morphologies remains limited. This study develops a scalable workflow to estimate the technical BIPV potential of roofs and facades within standardized 3 km × 3 km urban-core windows in 10 representative Chinese cities. Building footprints, height-related attributes, and functional tags derived mainly from OpenStreetMap were audited, cleaned, and completed through a hierarchical imputation strategy. A 2.5D urban geometry model was then used to estimate annual solar irradiation on building envelopes, with shading, orientation, and sky visibility explicitly considered. The results show that inter-city variation in BIPV potential is not governed by sunshine duration alone, but is strongly shaped by building density, height structure, envelope composition, and roof–facade contribution patterns. High total potential and high envelope-use efficiency do not necessarily occur in the same cities, indicating that total supply capacity and spatial deployment efficiency should be evaluated separately. The analysis further shows that facade-led BIPV pathways may be important in high-density urban cores, but facade-related estimates are sensitive to height-data completeness and usable-facade assumptions. These findings suggest that urban BIPV planning should move beyond aggregate solar-resource ranking and adopt morphology-aware, surface-specific, and data-quality-conscious assessment frameworks. The proposed workflow is intended for early-stage screening and cross-city comparison and provides a basis for identifying differentiated deployment priorities for roofs and facades in urban solar energy systems. Full article