Search Results (417)

In agriculture, climate change is the most critical global issue. It is widely acknowledged that addressing this issue poses a considerable challenge, primarily due to its multifaceted impact on regional economies and land management practices. The concept of Nature-based Solutions (NbS) provides a prosperous approach offering both adaptation and mitigation models. However, NbS implementation is often compromised by various natural and societal challenges. Vojvodina Province, the northern province of the Republic of Serbia, features a typical rural landscape where centuries of agricultural practice have led to significant environmental changes, with 70% of the territory converted to arable land. However, climate change has been demonstrated to induce increasingly extreme weather conditions, which in turn exacerbate the situation with regard to food production. This paper aims to examine the most prosperous ways for NbS implementation in Vojvodina Province. The preset study mapped areas suitable for the implementation of selected NbS on the territory of Vojvodina Province. Maps were created in QGIS, while data were extracted from various sources (CORINE Land Cover, OpenStreetMap, the Institute for Nature Conservation of Vojvodina Province, and EUNIS platform). The area suitable for NbS in Vojvodina amounts to 1,183,228 ha or 55.74%. An increase in the area dedicated to organic and regenerative agriculture is projected, with a predicted range of up to 5%. Finally, we have identified grazing as a desirable management option for grassland management, which we have mapped, and it could potentially be practiced on almost 10% of the territory. Moreover, the engagement of various stakeholders is crucial in the implementation of NbS over the territory of the rural landscape. Considering that neighboring countries are facing the same climate circumstances and a similar social context, the findings we have presented in the paper may be applied to the region of the southern part of the Pannonian Plain. Full article

This study introduces SquareSwish, a smooth, self-gated activation $f\left(x\right)=x\sigma {\left(x\right)}^2$, and benchmarks it against ten established activations (ReLU, LeakyReLU, ELU, SELU, GELU, Snake, LearnSnake, Swish, Mish, Hard-Swish) across six CNN architectures (EfficientNet-B1/B4, EfficientNet-V2-M/S, ResNet-50, and Xception) under a uniform transfer-learning protocol. Two geographically grounded datasets are used in this study. FuelRiskMap-TR comprises 7686 satellite images of urban fuel stations in Türkiye, which is semantically enriched with the OpenStreetMap context and YOLOv8-Small rooftop segmentation (mAP@0.50 = 0.724) to support AI-enabled, ICT-integrated risk screening. In a similar fashion, FuelRiskMap-UK is collected, comprising 2374 images. Risk scores are normalized and thresholded to form balanced High/Low-Risk labels for supervised training. Across identical training settings, SquareSwish achieves a top-1 validation accuracy of 0.909 on EfficientNet-B1 for FuelRiskMap-TR and reaches 0.920 when combined with SELU in a simple softmax-probability ensemble, outperforming the other activations under the same protocol. By squaring the sigmoid gate, SquareSwish more strongly attenuates mildly negative activations while preserving smooth, non-vanishing gradients, tightening decision boundaries in noisy, semantically enriched Earth-observation settings. Beyond classification, the resulting city-scale risk layers provide actionable geospatial outputs that can support inspection prioritization and integration with municipal GIS, offering a reproducible and low-cost safety-planning approach built on openly available imagery and volunteered geographic information. Full article

Spatial accessibility to education represents a key component of spatial justice, yet significant disparities persist between urban and rural areas in Romania. The present paper introduces the concept of education deserts as settlements where the population lacks proper access to education within a reasonable commuting distance and travel time, with a focus on high schools. Open-source demographic and institutional data and GIS-based spatial analysis were used in identifying education deserts across Romania. These were later evaluated based on a 20 min travel time or a 25 km distance threshold computed using OpenStreetMap API data. To assess the multidimensional nature of education deserts, a Composite Demand Index (CDI) and an Access Hardship Index (AHI) have been developed. Both were integrated into a final Education Desert Index (EDI) that captures unmet demand and spatial constraints. Results indicate that 34.3% of Romanian settlements (1092 LAU2s) and 15.2% of the high school-aged population reside in education deserts, found predominantly in the country’s North-East, South, and Centre regions. These areas coincide with rural, peripheral zones characterised by infrastructural deficits and low educational attainment. Findings reveal spatial inequities in upper secondary education provision between urban and rural communities. The present study offers a replicable methodological framework for evaluating educational accessibility and supports evidence-based policymaking aimed at reducing spatial disparities in education. Full article

Street network graphs model interconnected land transport infrastructure, including roads and intersections, enabling traffic analysis, route planning, and network optimization. Directed network graphs (digraphs) add directionality to these connections, reflecting one-way streets and complex traffic flows. While OpenStreetMap (OSM) offers extensive data, visualizing large-scale directed networks with complex junctions remains computationally challenging for browser-based tools. This paper presents an interactive visualization tool integrating OSM data with the New Zealand Transport Agency’s National Network Performance (NNP) analysis toolbox using PyDeck and WebGL. We introduce a directional offset algorithm to resolve edge overlaps and a geometry-aware node placement method for complex intersections. Experimental results demonstrate that our PyDeck implementation significantly outperforms existing solutions like Bokeh and OSMnx. On standard datasets, the system achieves up to 238× faster processing speeds and a 93% reduction in output file size compared to Bokeh. Furthermore, it successfully renders metropolitan-scale networks (∼1.3 million elements) where traditional visualisation tools fail to execute. This visualisation approach serves as a critical debugging instrument for NNP, allowing transport modellers to efficiently identify connectivity errors and validate the structural integrity of large-scale transport models. Full article

The urban heat island (UHI) effect poses significant challenges to cities worldwide, particularly in regions like Thessaloniki, Greece, where rising temperatures exacerbate urban living conditions. This study investigates the effectiveness of sustainable urban planning strategies in mitigating the UHI effect by analyzing the spatial distribution of Land Surface Temperature (LST) during the summer heatwave of 2023. Utilizing LANDSAT 8–9 satellite imagery processed with QGIS, we calculated LST, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). Additionally, urban structure data from OpenStreetMap (OSM) was integrated to assess the urban fabric. Our findings reveal significant spatial temperature variations, with densely built-up areas, such as the old town and industrial district, exhibiting higher LSTs compared to greener spaces. Based on these results, we propose targeted interventions, including the large-scale implementation of green roofs and the use of light-colored asphalts, which have shown potential for substantial LST reduction. This work underscores the importance of integrating these strategies into a standardized urban planning framework to enhance urban resilience, providing a model that can be applied to other European cities facing similar climate challenges. Full article

The rapid expansion of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) poses significant challenges for the integration of Unmanned Aircraft Systems (UAS) into dense urban environments, where safety risks and population exposure are particularly high. This study proposes and applies a methodology based on probabilistic assessment of both ground and air risk, grounded in the principles of safety management and the use of geospatial data from OpenStreetMap (OSM), official aeronautical charts, and digital urban models. The urban area is discretized into a spatial grid on which independent risks are calculated per cell and later combined through a cumulative probabilistic fusion model. The resulting risk estimates enable the construction of cost matrices compatible with path-search algorithms. The methodology is applied to a case study in Medellín, Colombia, connecting the Oviedo and San Diego shopping centers through Beyond Visual Line of Sight (BVLOS) operations of a DJI FlyCart 30 drone. Results show that planning with the A* algorithm produces safe routes that minimize exposure to critical areas such as hospitals and restricted air corridors, while maintaining operational efficiency metrics. This approach demonstrates a practical bridge between regulatory theory and operational practice in UAM corridor design, offering a replicable solution for risk management in urban scenarios. Full article

This paper presents the concept, implementation, and evaluation of FastFoam—a web-based inspection system designed for the technical assessment of buildings. Developed through international collaboration, FastFoam supports flexible inspection workflows, structured data collection, and integration with classification systems and geospatial data. The system enables civil engineers to create, customize, and manage inspection templates, store inspection results in a centralized database, and analyze inspection data using both descriptive and extensible analytical tools.To assess user needs and guide system development, a nationwide survey was conducted among Polish civil engineering professionals. The results confirmed strong interest in mobile and web-based inspection tools, as well as specific functional expectations regarding template customization, defect documentation, and automated reporting. The system architecture follows a multi-layered design with separate user, server, and external service layers. It supports modular data structures, role-based access, and integration with external platforms such as OpenStreetMap and BIM systems. A key innovation of FastFoam is its implementation of the FOAM (Function-Oriented Assessment Methodology), which enables temporal analysis and prediction of building condition over various timeframes. A case study demonstrates the application of FastFoam in a real-world building inspection in Poland. The evaluation confirmed the system’s practical usability while also revealing opportunities for future enhancements including AI-based defect detection, IoT integration, offline mobile functionality, and open data export. Full article

This study investigates the nonlinear, interactive, and temporally dynamic effects of urban form on street vitality within community-oriented metro station areas (MSAs) in Beijing. It offers potential reference value for other cities facing comparable challenges in MSA implementation and increasing motorization. This research addresses gaps in prior studies concerning the integration of multi-source data, nonlinearity, and diurnal variation. Utilizing an extended node-place-design framework, urban form is conceptualized through network, interface, and functional dimensions. The empirical analysis employs multi-source datasets, including 128,199 mobile device trips recorded in April 2024, OpenStreetMap for network data, Baidu points of interest for functional data, and Grasshopper for interface metrics, covering 183 street samples within a 1000 m radius of metro stations. Traditional regression models—such as ordinary least squares and spatial autocorrelation and cross-correlation—are used as baselines, while a novel gradient-boosting decision tree with latitude and longitude features is applied to enhance predictive performance. The results indicate that key contributors include road network density (16.89%), road intersections (10.56%), and point-of-interest density (9.74%), with Shapley Additive Explanations dependence plots demonstrating nonlinear thresholds. The analyses reveal synergistic or antagonistic interactions among features. Temporal fluctuations in feature importance further support the presence of diurnal dynamics. The study provides insights for time-sensitive urban planning aimed at enhancing MSA vitality, sustainability, and resident quality of life, while acknowledging that the conclusions are context-specific to Beijing and require additional validation in other urban environments. Full article

In the context of sustainable development, the concept of active mobility plays a key role in modern urban areas. To evaluate active mobility in these areas, we formulate a framework for characterising active mobility by calculating street network indices using global, free, and open data. This framework comprises the download and processing of pedestrian, cycling, driving, and public transport street networks from OpenStreetMap, the selection of street network indices from the academic literature, and their implementation and calculation. A total of 50 indicators are reported for each urban area distributed in eight index types, including thematic variables, proximity to Points of Interest (POIs), proximity to public transport, intersection density, street density, street length, link–node ratio, circuity, slope, and orientation entropy. To test the framework, we calculate street network indices for pedestrian and cycling networks for the urban areas of 176 cities from around the world. The resulting dataset is published as open data. An analysis of the calculated indices indicates that cities in higher-income economies generally exhibit better conditions for active mobility, especially in Europe, attributed to better map completeness, and to more compact and connected urban areas where it is easier to access amenities and public transport. Full article

Packet delivery in vehicular ad hoc networks degrades under realistic road dynamics, where mobility and local density vary over time and across road layouts. This study revisits route lifetime control in AODV and introduces Vehicular Traffic Condition-Based AODV, which adjusts the Active Route Timeout and the Delete Period Constant online at each HELLO reception using locally observable cues on neighbor density and short-term speed variation. The design is empirically informed by OpenStreetMap and SUMO mobility with OMNeT++/Veins/INET co-simulation. The analysis highlights two recurrent patterns that guide the method: (i) an intermediate neighbor-density range—roughly from the mid-teens to about twenty neighbors—where average speed tends to vary more rapidly; and (ii) a distribution of short-term speed-change magnitudes, sampled at the instants of HELLO reception, that is concentrated within a narrow interval with a light upper tail. Accordingly, the proposed method increases or decreases route-entry lifetimes with heightened responsiveness inside this density range, while applying conservative updates elsewhere to mitigate oscillations. Evaluation across multiple vehicular-traffic conditions spanning three fleet sizes (200, 300, 400 vehicles) and three speed-limit settings (10, 20, 30 km/h) demonstrates consistent packet delivery ratio gains over conventional AODV and close tracking of the best static lifetime configurations identified per condition. The gains are attributable to timely pruning of unstable paths and sustained retention of stable paths, which increases valid forwarding opportunities without centralized coordination. Full article

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes. Full article

Collaborative mapping has emerged in recent decades as a key practice for producing open geospatial knowledge and fostering critical citizenship. However, several studies have shown that these platforms may reproduce existing gender inequalities, both in terms of participation and representation. This article examines the potential of collaborative feminist cartography as a strategy for making inequalities visible and promoting gender equality in public space. Methodologically, the study focuses on the project Las Calles de las Mujeres, developed by Geochicas OSM, combining quantitative analysis of street naming in urban development with qualitative implementation in educational contexts. A global overview of 32 cities in 11 countries is provided, with a detailed case study of 11 Spanish cities. Results confirm the persistence of a significant gender gap in urban toponymy: streets named after men not only outnumber those dedicated to women but are also on average longer, more central, and symbolically more prominent. Educational experiences in Spain provide learning outcomes and demonstrate that collaborative mapping strengthens spatial thinking, digital competence, and critical awareness, linking geography education to the Sustainable Development Goals (SDG 5 and SDG 11). The article concludes that feminist mapping initiatives are simultaneously pedagogical, social, and political tools, capable of fostering more inclusive and sustainable cities. Full article

Accurate and up-to-date data on built-up areas are crucial for urban planning, disaster management, and sustainable development, yet Romania still lacks a unified, official database. In this study we integrated the three widely used global data sources—OpenStreetMap (OSM), Microsoft Building Footprints (MSBFs), and Global Human Settlement Layer Built-up surface (GHS)—onto a 10 m resolution raster grid and applied this consistently at the national scale across 3181 settlement polygons to produce a more accurate, unified ensemble model for Romania. The methodological basis was Triple Collocation Analysis (TCA), extended with ETC/CTC to estimate per-settlement scale factors, enabling the quantification and optimal weighting of the relative errors and accuracy in the absence of independent reference data. Weight patterns vary by settlement type: OSM receives relatively higher weights in smaller rural settlements with less redundant error; in municipalities the stronger OSM–MSBF correlation reduces both of their weights and increases the GHS share; cities exhibit a more balanced weighting. At cell level, the ensemble provides uncertainty quantification via confidence intervals that typically range from 2% to 14% at settlement scale. The resulting model—like any model—does not perfectly reflect reality; however, the ensemble improves the accuracy and timeliness of the available data. The resulting model is replicable and updatable with newer data, making it suitable for numerous practical applications, especially in spatial development and risk analysis. Full article

Urban green spaces (UGS) are pivotal to urban sustainability, yet their morphology—patch size, shape, and configuration—remains insufficiently linked to institutional drivers. We investigate how land tenure strength shapes UGS morphology across 36 cities in nine countries. Using OpenStreetMap data, we delineate UGS and compute landscape metrics (AREA, PARA, SHAPE, FRAC, PAFRAC) via FRAGSTATS; we develop a composite index of land tenure strength capturing ownership, use-right duration, expropriation compensation, and government land governance capacity. Spearman’s rank correlations indicate a scale-dependent coupling: stronger tenure is significantly associated with micro-scale patterns—smaller patch areas and more complex, irregular boundaries—consistent with fragmented ownership and higher transaction costs, whereas macro-scale indicators (e.g., overall green coverage/connectivity) show weaker sensitivity. These findings clarify an institutional pathway through which property rights intensity influences the physical fabric of urban nature. Policy implications are twofold: in high-intensity contexts, flexible instruments (e.g., transferable development rights, negotiated acquisition, ecological compensation) can maintain network connectivity via embedded, fine-grain interventions; in low-intensity contexts, one-off land assembly can efficiently deliver larger, regular green cores. The results provide evidence-based guidance for aligning green infrastructure design with diverse governance regimes and advancing context-sensitive sustainability planning. Full article

Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a high-resolution, hourly emission inventory at the road-segment level for six major expressways in Shanghai, one of China’s leading port cities. The emission estimates are derived using a locally adapted COPERT V model, calibrated with HDDT GPS trajectory data and detailed road network information from OpenStreetMap. The inventory quantifies emissions of CO₂, NO_x, CO, PM, and VOCs, highlighting distinct temporal and spatial variation patterns. Weekday emissions consistently exceed those of weekends, with three prominent traffic-related peaks occurring between 5:00–7:00, 10:00–12:00, and 14:00–16:00. Spatial analysis identifies the G1503 and S20 expressways as major emission corridors, with S20 exhibiting particularly high emission intensity relative to its length. Combined spatiotemporal patterns reveal that weekday emission hotspots are more concentrated, reflecting typical freight activity cycles such as morning dispatch and afternoon return. The findings provide a scientific basis for formulating more precise emission control measures targeting HDDT operations in urban port environments. Full article