Search Results (2,122)

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

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

Despite its potential danger, site amplification effects are often neglected in seismic hazard analysis. Appropriate amplification factors can be determined from shear wave velocity, but impracticality in in situ measurements leads to reliance on regional correlation with geotechnical parameters such as SPT N-value. Modified power law and logarithmic equations were derived from past correlation studies to determine V_s30 values for each borehole location in the City of Manila. V_s30 profiles were spatially interpolated using the inverse-distance weighted and thin-spline methods to approximate the variation in shear wave velocities and add more detail to the existing contour map for soil profile classification across Metro Manila. Statistical analysis of the interpolated models indicates percentage differences ranging from 0 to 10% with a normalized root mean square error of nearly 5%. Generated equations and geospatial models in the study may be used as a basis for a seismic microzonation model for Metro Manila, considering other geological and geophysical layers. 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

Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models (DEMs) with hydrological parameters, applying weighted sum analysis to classify 18 sub-watersheds into different flood priority levels. Morphometric parameters, including basin relief, drainage density, and slope, were analyzed to establish correlations between watershed morphology and flood susceptibility. The results indicate that approximately 74.38% of the watershed exhibits high to very high flood risk, with the most vulnerable sub-watersheds characterized by steep slopes, high drainage densities, and compact morphometric configurations. The correlation matrix confirms that watershed topography significantly influences surface runoff behavior, underscoring the necessity of incorporating geospatial analysis into flood risk assessment frameworks. The classification of sub-watersheds into priority levels provides a scientific basis for optimizing resource allocation in flood mitigation strategies. This study highlights the importance of integrating advanced geospatial technologies, such as GISs and remote sensing, into hydrological risk assessments. The findings emphasize the need for proactive watershed management, including the use of real-time monitoring and digital tools for climate adaptation. Future research should explore the influence of land-use changes and climate variability on flood dynamics to enhance predictive modeling. These insights contribute to evidence-based decision-making for disaster risk reduction, reinforcing resilience in climate-sensitive regions. 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

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

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

Japanese knotweed (Fallopia japonica) is a noxious invasive plant species that requires scalable and precise monitoring methods. Current visually based ground surveys are resource-intensive and inefficient for detecting Japanese knotweed in landscapes. This study presents a transformer-based semantic segmentation framework for the automated detection of Japanese knotweed patches using high-resolution RGB imagery acquired with unmanned aerial vehicles (UAVs). We used the Twins Spatially Separable Vision Transformer (Twins-SVT), which utilizes a hierarchical architecture with spatially separable self-attention to effectively model long-range dependencies and multiscale contextual features. The model was trained on 6945 annotated aerial images collected in three sites infested with Japanese knotweed in West Virginia, USA. The results of this study showed that the proposed framework achieved superior performance compared to other transformer-based baselines. The Twins-SVT model achieved a mean Intersection over Union (mIoU) of 94.94% and an Average Accuracy (AAcc) of 97.50%, outperforming SegFormer, Swin-T, and ViT. These findings highlight the model’s ability to accurately distinguish Japanese knotweed patches from surrounding vegetation. The method and protocol presented in this research provide a robust, scalable solution for mapping Japanese knotweed through aerial imagery and highlight the successful use of advanced vision transformers in ecological and geospatial information analysis. Full article

This study examines the use of a spatial multi-criteria approach based on GIS and AHP techniques to model landslide risk in the Utcubamba river basin, Peru. The methodology consisted of selecting twelve triggering variables: slope angle, geology, precipitation, distance to faults, drainage density, TWI, relative relief, profile curve, land use, elevation, distance to roads, and distance to population centers. These variables were then analyzed using the AHP method and then integrated into a GIS environment, where the weighted linear combination (WLC) method was used to map landslide risk. The risk was categorized into five classes, ranging from very low (1) to very high (5). The main results indicate that 32.81% of the area analyzed in the Utcubamba river basin presents a high and very high risk of landslides. The high-risk areas are mainly located in the southern part of the basin and coincide with areas with steep slopes, high rainfall, and proximity to population centers or communication routes. The model generated was highly accurate (AUC of 0.82), confirming that the integration of the AHP method with GIS allows for the precise identification of critical areas, which is useful for territorial planning, the prioritization of interventions, and emergency management, making it a reliable and replicable methodology in other parts of Peru. Full article

Complex event detection (CED) adds value to camera stream data in various applications such as workplace safety, task monitoring, security, and health. Recent CED frameworks have addressed the issues of limited spatiotemporal labels and costly training by decomposing the CED into low-level features, as well as spatial and temporal relationship extraction. However, these frameworks suffer from high resource costs, low scalability, and an increased number of false positives and false negatives. This paper proposes GICEDCAM, which distributes CED across edge, stateless, and stateful layers to improve scalability and reduce computation cost. Additionally, we introduce a Spatial Event Corrector component that leverages geospatial data analysis to minimize false negatives and false positives in spatial event detection. We evaluate GICEDCAM on 16 camera streams covering four complex events. Relative to a strong open-source baseline configured for our setting, GICEDCAM reduces end-to-end latency by 36% and total computational cost by 45%, with the advantage widening as objects per frame increase. Among corrector variants, Bayesian Network (BN) yields the lowest latency, Long Short-Term Memory (LSTM) achieves the highest accuracy, and trajectory analysis offers the best accuracy–latency trade-off for this architecture. Full article

Territorial transformations in Indigenous regions are shaped by intersecting ecological, political, and cultural dynamics. In San Juan Bautista Valle Nacional, Oaxaca, the construction of the Cerro de Oro dam disrupted river flows, displaced livelihoods, and triggered the decline of irrigated agriculture. This study examines the long-term impacts of these changes on land use, demographics, and cultural practices, emphasizing women’s contributions to community resilience. Using a mixed-methods approach, the study integrates geospatial analysis (1992–2021), census data (2000–2020), documentary review, and ethnographic fieldwork, including participatory mapping. Results show a shift toward seasonal rainfed agriculture, fluctuating forest cover, and a rise in female-headed households. Women have emerged as central actors in adapting to change through practices such as seed saving, agroforestry, and backstrap-loom weaving. These spatially grounded practices, enacted across varied socio-ecological zones, sustain food systems, preserve biodiversity, and reinforce biocultural memory. Although often overlooked in formal governance, women’s territorial agency plays a vital role in shaping land use and community adaptation. This research highlights the need to recognize Indigenous women’s roles in managing change and sustaining territorial heritage. Acknowledging these contributions is essential for building inclusive, culturally grounded, and sustainable development pathways in regions facing structural and environmental pressures. Full article

Ecotourism, as a resilient and sustainable form of tourism, plays an increasingly vital role in regional economic growth and ecological conservation, particularly in the face of challenges such as climate change and rapid urbanization. This study employs spatial-temporal analysis tools including GIS, Fragstats, and GeoDa to examine the dynamic evolution of ecotourism suitability levels (ESL) and landscape patterns (LP) in the Yangtze River Delta (YRD) from 2002 to 2022. By incorporating spatial autocorrelation analysis, the relationship between ESL and LP is investigated to assess the adaptive capacity of the regional ecotourism system. The results reveal the following: (1) Overall Trends: ESL in the YRD has generally increased over the past two decades, with expansions observed in both high and very low suitability areas, while areas of low suitability have contracted. (2) Spatial Patterns: Core cities such as Shanghai, Hangzhou, Nanjing, and Hefei exhibit high ESL; however, these areas also face intensified landscape fragmentation and decreased ecological connectivity. (3) Landscape Patterns: The region has experienced increasing landscape fragmentation and diversity, particularly in economically advanced zones, posing significant challenges to ecological resilience. (4) Spatial Clustering: Notable spatial clustering of ESL and LP indices is identified in highly urbanized areas, underscoring the necessity for adaptive landscape planning and flexible policy frameworks. This study provides empirical evidence and strategic recommendations to enhance the resilience and sustainability of ecotourism in rapidly urbanizing regions, supporting adaptive responses to crises and informed long-term decision-making. Full article