Search Results (2,911)

Flash floods arise from the interaction of rugged topography, short-duration intense rainfall, and rapid flow concentration. Conventional risk mapping often builds empirical indices with expert-assigned weights or trains supervised models on historical event inventories—approaches that degrade in data-scarce regions. We propose a fully data-driven, unsupervised Geographic Information System (GIS) framework based on fractional order k-means, which clusters multi-dimensional geospatial features without labeled flood records. Five raster layers—elevation, slope, aspect, 24 h maximum rainfall, and distance to the nearest stream—are normalized into a feature vector for each 30 m × 30 m grid cell. In a province-scale case study of Zhejiang, China, the resulting risk map aligns strongly with the observations: 95% of 1643 documented flash flood sites over the past 60 years fall within the combined high- and medium-risk zones, and 65% lie inside the high-risk class. These outcomes indicate that the fractional order distance metric captures physically realistic hazard gradients while remaining label-free. Because the workflow uses commonly available GIS inputs and open-source tooling, it is computationally efficient, reproducible, and readily transferable to other mountainous, data-poor settings. Beyond reducing subjective weighting inherent in index methods and the data demands of supervised learning, the framework offers a pragmatic baseline for regional planning and early-stage screening. Full article

Disaster risk reduction requires efficient flood control in lowland and flood-prone areas, especially in agricultural areas like the Bang Rakam model area in Phitsanulok province, Thailand. In order to improve flood prediction and response, this study proposes the creation of a low-cost, real-time water-level monitoring integrated with spatial data analysis using Geographic Information System (GIS) technology. Ten ultrasonic sensor-equipped monitoring stations were installed thoughtfully around sub-catchment areas to provide highly accurate water-level readings. To define inundation zones and create flood depth maps, the sensors gather flood level data from each station, which is then processed using a 1-m Digital Elevation Model (DEM) and Python-based geospatial analysis. In order to create dynamic flood maps that offer information on flood extent, depth, and water volume within each sub-catchment, an automated method was created to use real-time water-level data. These results demonstrate the promise of low-cost IoT-based flood monitoring devices as an affordable and scalable remedy for communities that are at risk. This method improves knowledge of flood dynamics in the Bang Rakam model area by combining sensor technology and spatial data analysis. It also acts as a standard for flood management tactics in other lowland areas. The study emphasizes how crucial real-time data-driven flood monitoring is to enhancing early-warning systems, disaster preparedness, and water resource management. Full article

This research presents the development of a thematic map of the Archdiocese of Đakovo–Osijek, one of five Roman Catholic archdioceses in the Republic of Croatia. The map delineates contemporary ecclesiastical boundaries and key religious sites, while drawing on both historical and modern geospatial datasets. In addition to the final cartographic product, this paper emphasizes the methodological process of map creation, including data acquisition, historical georeferencing, symbol design, and GIS integration. The workflow is structured within a Historical GIS framework to ensure positional and semantic accuracy. The aim is not only to present a high-quality spatial representation but also to propose a reproducible methodology adaptable to other religious cartographic projects. Full article

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects. Full article

The expansion of solar photovoltaic (PV) energy is a central pillar of Italy’s climate and energy transition strategy. However, the actual availability of land for PV deployment is heavily influenced by a complex regulatory framework that imposes numerous spatial exclusions. This study presents a comprehensive geospatial analysis of exclusion zones for ground-mounted PV installations across Italy, integrating data from national regulations, environmental protection laws, and cultural heritage inventories. Using a vector-based overlay approach, we categorized constraints into six groups: natural assets, landscape protection, cultural heritage, natural hazards, environmental buffers, and infrastructural safety zones. The analysis shows that only approximately 26% of Italy’s land area remains available for PV development. Regional disparities are pronounced, with southern regions such as Sicilia and Puglia offering the highest availability, while northern and central regions face severe limitations due to dense overlays of protected landscapes and heritage sites. These findings offer quantitative support to the often-cited claim that Italy’s strict land-use regulations are a significant barrier to renewable energy development. The study highlights the need for more flexible, spatially informed regulatory frameworks to reconcile conservation priorities with the urgency of decarbonization. Full article

We present a novel deep learning framework for high-resolution semantic segmentation, designed to interpret complex visual environments such as cities, rural areas, and natural landscapes. Our method integrates conic geometric embeddings, which is a mathematical approach for capturing spatial relationships, with belief-aware learning, a strategy that adapts model predictions when input data are uncertain or change over time. A multi-scale refinement process further improves boundary accuracy and detail preservation. The proposed model, built on a hybrid Vision Transformer (ViT) backbone and trained end-to-end using adaptive optimization, is evaluated on four benchmark datasets including EDEN, OpenEarthMap, Cityscapes, and iSAID. It achieves 88.94% Accuracy and $R^2$ of 0.859 on EDEN, while surpassing 85.3% Accuracy on Cityscapes. Ablation studies demonstrate that removing Conic Output Embeddings causes drops in Accuracy of up to 2.77% and increases in RMSE, emphasizing their contribution to frequency-aware generalization across diverse conditions. On OpenEarthMap, our model achieves a mean IoU of 73.21%, outperforming previous baselines by 2.9%, and on iSAID, it reaches 80.75% mIoU with improved boundary adherence. Beyond technical performance, the framework enables practical applications such as automated landscape analysis, urban growth monitoring, and sustainable environmental planning. Its consistent results across three independent runs demonstrate both robustness and reproducibility, offering a reliable tool for large-scale geospatial and environmental modeling. Full article

The process of social globalization and urbanization has developed rapidly in China, and the tension between economic development and the eco-environment is becoming increasingly tense, posing a major challenge to the sustainable development strategy of the Shandong Peninsula Urban Agglomeration (SPUA). Coordination development between economic development and habitat quality has become essential for preserving ecological stability and advancing long-term regional sustainability. This study constructed the optimal regression model to measure GDP density using night-time lighting data and economic statistical data and calculated habitat quality at the grid scale with the InVEST model. The spatiotemporal dynamics and driving factors of the coupling coordination between economy and habitat quality (EHCCD) were revealed using the coupling coordination degree model and the Geo-detector model. The results show that (1) between 2000 and 2020, the spatial pattern of GDP density has evolved from a single-core to a multi-core networked development. (2) The habitat quality of the SPUA exhibited a spatial pattern high in the east and low in the west, showing a downward trend. (3) The synergistic effect between GDP density and habitat quality was strengthened continuously, showing an overall strengthening tendency. (4) Driving factors’ influence on the EHCCD showed evident differences; socio-economic factors such as built-up area especially had greater explanatory power for the EHCCD; the interaction factors had shifted from socio-economic dominance to synergistic dominance of natural and human factors. This study not only overcomes the limitations imposed by administrative boundaries on assessing inter-regional coupling coordination but also provides fundamental data support for cross-regional cooperation, thereby advancing the sustainable development goal of the SPUA. Full article

Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management. Full article

Electromagnetic induction (EMI) devices have become increasingly popular for their soil bulk properties, soil nutrient status, and use in taking non-invasive soil salinity measurements. However, the high cost of data acquisition (DAQ) systems has been a significant barrier to the widespread adoption of these devices. In this study, we addressed this challenge by developing a cost-effective, easy-to-use, open-source DAQ system, transferable to the end user. This system employs a Raspberry Pi 4 model, paired with various components, to monitor the speed and position of the EM38 (Geonics Ltd, Mississauga, ON, Canada) and compare these with a proprietary CR1000 system. Through our results, we demonstrate that the low-cost DAQ system can successfully extract the analogical signal from the device, which is strongly responsive to the variation in the soil’s physical properties. This cost-effective system is characterized by increased flexibility in software processes and provides performance comparable to the proprietary system in terms of its geospatial data and ECb measurements. This was validated by the strong correlation (R² = 0.98) observed between the data collected from both systems. With our zoning analysis, performed using the Kriging technique, we revealed not only similar patterns in the ECb data but also similar patterns to the Normalized Difference Vegetation Index (NDVI) map, suggesting that soil physical characteristics contribute to variability in crop vigor. Furthermore, the developed web application enabled real-time data monitoring and visualization. These findings highlight that the open-source DAQ system is a viable, cost-effective alternative for soil property monitoring in precision farming. Future enhancements will focus on integrating additional sensors for plant vigor and soil temperature, as well as refining the web application, supporting zone classification based on the use of multiple parameters. Full article

This study addresses the challenge of reliably identifying Public Urban Green Spaces (PUGS) in metropolitan areas, a key requirement for advancing equitable access to green infrastructure and monitoring progress toward SDG 11.7 and WHO recommendations. In the absence of consistent local datasets, we propose the Candidate Urban Green Area (CUGA) method, which integrates OpenStreetMap and Copernicus Urban Atlas data through a structured, transparent workflow. The method applies spatial and functional filters to isolate green spaces that are publicly accessible, meet minimum size and usability criteria, and are embedded within the urban fabric. We validate CUGA in the Dublin Region using a stratified random sample of 1-ha cells and compare its performance against five alternative datasets. Results show that CUGA achieves the highest classification accuracy, spatial coverage, and statistical robustness across all counties, significantly outperforming administrative, crowdsourced, and satellite-derived sources. The method also delivers greater net spatial impact in terms of green area, catchment coverage, and residential land intercepted. These findings support the use of CUGA as a reliable and transferable tool for urban green space planning, policy evaluation, and sustainability reporting, particularly in data-scarce or fragmented governance contexts. Full article

Mass gatherings such as the annual Hajj pilgrimage in Makkah, Saudi Arabia, generate extreme, short-term anthropogenic emission loads with significant air quality and public health implications. This study assesses the spatiotemporal dynamics of key atmospheric pollutants—including nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), formaldehyde (HCHO), and aerosols—across Makkah and its holy sites before and during the Hajj seasons of 2023 and 2024. Using high-resolution Sentinel-5P TROPOMI satellite data, pollutant fields were reconstructed at 100 m spatial resolution via cloud-based geospatial analysis on the Google Earth Engine. During Hajj 2023, spatially resolved NO₂ concentrations ranged from 15.4 μg/m³ to 38.3 μg/m³ with an average of 24.7 μg/m³, while SO₂ during the 2024 event peaked at 51.2 μg/m³ in key hotspots, occasionally exceeding World Health Organization (WHO) guideline values. Aerosol index values showed episodic surges (up to 1.43), particularly over transportation corridors, parking areas, and logistics facilities. CO concentrations reached values as high as 1069.8 μg/m³ in crowded zones, and HCHO concentrations surged up to 9.99 μg/m³ during peak periods. Quantitative correlation analysis revealed that during Hajj, atmospheric chemistry diverged from urban baseline: the NO₂–SO₂ relationship shifted from strongly negative pre-Hajj (r = −0.74) to moderately positive during the event (r = 0.35), while aerosol–HCHO correlations intensified negatively from r = −0.23 pre-Hajj to r = −0.50 during Hajj. Meteorological analysis indicated significant positive correlations between wind speed and NO₂ (r = 0.35) and wind speed and CO (r = 0.35) during 2024, demonstrating that extreme emission rates overwhelmed typical dispersive processes. Relative humidity was positively correlated with aerosol loading (r = 0.37), pointing to hygroscopic growth patterns. These results quantitatively demonstrate that Hajj drives a distinct, event-specific pollution regime, characterized by sharp increases in key pollutant concentrations, altered inter-pollutant and pollutant–meteorology relationships, and spatially explicit hotspots driven by human activity and infrastructure. The integrated satellite–meteorology workflow enabled near-real-time monitoring in a data-sparse environment and establishes a scalable framework for evidence-based air quality management and health risk reduction in mass gatherings. Full article

Accurate and up-to-date morphometric data on lakes are crucial for hydrological modeling, ecosystem monitoring, and sustainable water resource management. This study presents the first centimeter-scale, high-resolution bathymetric model of Lake Markakol (eastern Kazakhstan), generated using advanced hydroacoustic and geospatial techniques. The primary objective was to reassess key morphometric parameters—surface area, depth, volume, and shoreline configuration—more than six decades after the only existing survey from 1962. High-density depth data were acquired with a Lowrance HDS-12 Live echo sounder, achieving vertical precision of ±0.17 m, and processed using ReefMaster and ArcGIS to produce a three-dimensional, hydrologically correct model of the lake basin. Compared with archival data, results show that while the surface area (455.365 ± 0.005 km²), length (38.304 ± 0.002 km), and width (19.138 ± 0.002 km) have remained stable, the maximum depth is lower (24.14 ± 0.17 m vs. 27 m), and the total water volume is slightly higher (6.667 ± 0.025 km³ vs. 6.37 km³). These differences highlight both the limitations of historical lead-line surveys and the enhanced accuracy of modern hydroacoustic and GIS-based methods. The workflow developed here is transferable to other remote alpine lakes, providing an invaluable baseline for limnological research, ecological assessment, hydrodynamic modeling, and long-term water resource management strategies in data-scarce mountain regions. Full article

Point cloud segmentation underpins various applications in geospatial analysis, such as autonomous navigation, urban planning, and management. Kernel Point Convolution (KPConv) has become a de facto standard for such tasks, yet its fixed geometric kernel limits the modeling of fine-grained contextual relationships—particularly in heterogeneous, real-world point cloud data. In this paper, we introduce the adaptation of a Local Contextual Attention (LCA) mechanism for the KPConv network, with reweighting kernel coefficients based on local feature similarity in the spatial proximity domain. Crucially, our lightweight design preserves KPConv’s distance-based weighting while embedding adaptive context aggregation, improving boundary delineation and small-object recognition without incurring significant computational or memory overhead. Our comprehensive experiments validate the efficacy of the proposed LCA block across multiple challenging benchmarks. Specifically, our method significantly improves segmentation performance by achieving a 20% increase in mean Intersection over Union (mIoU) on the STPLS3D dataset. Furthermore, we observe a 16% enhancement in mean F1 score (mF1) on the Hessigheim3D benchmark and a notable 15% improvement in mIoU on the Toronto3D dataset. These performance gains place LCA-KPConv among the top-performing methods reported in these benchmark evaluations. Trained models, prediction results, and the code of LCA are available in a GitHub opensource repository. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article