Search Results (178)

Lateral passing distance (LPD) when motor vehicles overtake cyclists is a key safety metric, yet infrastructure-aware evidence remains limited. This study analyses 11,399 overtaking measurements from Austria’s OpenBikeSensor (OBS) project, spatially linked to the national road graph (GIP), with urban and rural networks examined separately. LPD was treated as a continuous dependent variable, and bivariate relationships were tested using nonparametric methods: Spearman’s rho/Kendall’s tau for metric predictors (speed limit, lane width, number of lanes) and Kruskal–Wallis tests with Dunn–Holm post hoc adjustments for categorical factors (Functional Road Class, Road Configuration, Infrastructure Type). Effect sizes and confidence intervals supported substantive interpretation. LPD was higher in rural than urban contexts, with compliance to Austria’s 2023 legal thresholds averaging 40% in cities (≥1.5 m) and 19% in rural areas (≥2.0 m). Positive correlations were found between LPD and lane width, speed limit, and functional class. The findings highlight infrastructure-sensitive patterns in sensor-generated LPD and emphasise the importance of clear cyclist allocation or physical separation, especially where high speeds or spatial constraints increase close-passing risk. Full article

Soil degradation in high-altitude livestock systems—driven by acidification, compaction, low water retention and nutrient loss—reduces forage productivity and limits the sustainability of grazing-based production. These constraints highlight the need for spatial tools capable of prioritising soil interventions and guiding more efficient land management. The objective of this study was to develop a spatial analysis model to identify and rank soil management priorities in a high-altitude livestock farm. A total of 441 georeferenced observations were collected using portable sensors to measure pH, electrical conductivity, water retention capacity and soil compaction. The data were processed through GIS interpolation, cartographic overlay and reclassification techniques to assign intervention levels across the landscape. The results indicated that 70% of the area presented moderately acidic soils, 32% required improvements in water retention, and 67% exhibited moderate compaction. The proposed model is replicable, operationally simple and suitable for site-specific decision-making. Overall, this study provides a technical tool that supports extension programmes, territorial planning and sustainable livestock management. Full article

Recent advances in vibration-based pavement assessment have enabled the low-cost monitoring of road conditions using inertial sensors and machine learning models. However, most studies focus on isolated tasks, such as roughness classification, without integrating statistical validation, anomaly detection, or maintenance prioritization. This study presents a unified framework for road roughness severity classification and predictive maintenance using multi-axis accelerometer data collected from urban road networks in Pretoria, South Africa. The proposed pipeline integrates ISO-referenced labeling, ensemble and deep classifiers (Random Forest, XGBoost, MLP, and 1D-CNN), McNemar’s test for model agreement validation, feature importance interpretation, and GIS-based anomaly mapping. Stratified cross-validation and hyperparameter tuning ensured robust generalization, with accuracies exceeding 99%. Statistical outlier detection enabled the early identification of deteriorated segments, supporting proactive maintenance planning. The results confirm that vertical acceleration (accel_z) is the most discriminative signal for roughness severity, validating the feasibility of lightweight single-axis sensing. The study concludes that combining supervised learning with statistical anomaly detection can provide an intelligent, scalable, and cost-effective foundation for municipal pavement management systems. The modular design further supports integration with Internet-of-Things (IoT) telematics platforms for near-real-time road condition monitoring and sustainable transport asset management. Full article

Gas-insulated switchgear (GIS) serves as the core equipment in substations. Its partial discharge detection requires ultrasonic sensors to be precisely aligned with millimeter-level measurement points. However, existing technologies face three major bottlenecks: the lack of surface texture on GIS makes visual feature extraction difficult; strong electromagnetic interference in substations causes image noise and loss of feature point tracking; and fixed gain control easily leads to end-effector jitter, reducing positioning accuracy. To address these challenges, this paper first employs AprilTag visual markers to define GIS measurement point features, establishing an image-based visual servo model that integrates GIS surface curvature constraints. Second, it proposes an adaptive gain algorithm based on model predictive control, dynamically adjusting gain in real-time according to visual error, electromagnetic interference intensity, and contact force feedback, balancing convergence speed and motion stability. Finally, experiments conducted on a GIS inspection platform built using a Franka Panda robotic arm demonstrate that the proposed algorithm reduces positioning errors, increases positioning speed, and improves positioning accuracy compared to fixed-gain algorithms, providing technical support for the engineering application of GIS partial discharge detection robots. Full article

Partial discharge (PD) is not only the primary manifestation of insulation deterioration in gas-insulated switchgear (GIS) but also a critical indicator of the equipment’s insulation condition. PD in GIS typically occurs at media interfaces such as the surface of the basin insulator and is characterized by high randomness and low amplitude. Conventional built-in ultra-high frequency sensors exhibit limitations in early warning and detection performance. This study proposes and demonstrates a self-sensing shielding ring embedded within the basin insulator, functioning as a novel UHF sensor. Finite-difference time-domain (FDTD) is a numerical method used to solve problems involving electromagnetic fields. Based on actual GIS structural parameters, a FDTD simulation platform is constructed and a built-in sensor is used as a control to evaluate the receiving performance of the self-sensing shielding ring for PD signals. Time-domain array simulations are conducted to investigate the influence of radial, angular and axial positions on the observed performance. The results show that the proposed shielding ring exhibits broadband and low-reflection characteristics, achieving an average S₁₁ of −6.347 dB, which is significantly lower than those of the built-in sensors (−1.270 dB and −1.274 dB). The results demonstrate that the self-sensing shielding ring enables high sensitivity and the wideband detection of partial discharge, providing a new design approach and technical foundation for online early-warning systems in GIS. Full article

The general spread of water safety awareness and enforcement often masks the escalating risks of emerging contaminants (ECs) that evade standard detection and monitoring techniques. Traditional monitoring infrastructures depend heavily on localized laboratory-based testing, which is expensive, time-consuming, and reliant on specialized infrastructure and skilled personnel. While specific types of ECs and detection technologies have been examined in numerous studies, a significant gap remains in compiling and commenting on this information in a concise framework that incorporates global impact and monitoring strategies. We aimed to compile and highlight the impact ECs have on global water safety and how advanced sensor technologies, when integrated with digital tools such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), geographic information systems (GIS), and cloud-based analytics, can enhance real-time EC detection and monitoring. Recent case studies were reviewed for the assessment of EC types, global contamination, and current state-of-the-art for EC detection and their limitations. An emphasis has been placed on areas that remain unaddressed in the current literature: a cross-disciplinary integration of integrated sensor platforms, multidisciplinary research collaborations, strategic public–private partnerships, and regulatory bodies engagement will be essential in safeguarding public health, protecting aquatic ecosystems, and ensuring the quality and resilience of our water resources worldwide. Full article

Eddy covariance measurements are increasingly utilized for assessing the exchange of matter and energy between ecosystems and the atmosphere across various time scales, ranging from hours to years. The flux footprint represents the area observable by flux tower sensors and illustrates how the surface influences the measured flux. Flux footprint models describe both the spatial extent and the specific location of the surface area contributing to the observed turbulent fluxes. In this study, we applied a simple two-dimensional parameterization for flux footprint prediction (FFP), developed by Kljun et al. to identify the location of peak footprint contribution every half hour over a six-year period. Monthly cluster analysis was performed on these data. Using an open-source geographic information system (GIS) software, the resulting clusters were overlaid on a base map of the site obtained from the Estonian Land Board, where different compartments have varying growth stages and species compositions. Our main objective was to integrate forest inventory data with ecosystem exchange and productivity data continuously recorded by the eddy covariance measurement tower at Järvselja, Estonia. This integration enabled spatially explicit visualization of half-hourly flux contributions using geographic information system software. Full article

This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a freely accessible weather API, eliminating the need for ground-based IoT sensors. The proposed algorithm integrates FAO-56 evapotranspiration principles and water stress indices to accurately forecast irrigation requirements across the four critical growth stages of cotton. Supervised learning algorithms, including Gradient Boosting, Random Forest, and Logistic Regression, were evaluated, with Random Forest indicating better predictive accuracy with a coefficient of determination (R²) exceeding 0.92 and a root mean square error (RMSE) of approximately 415 kg/ha, owed its capacity to handle complex, non-linear relations, and feature interactions. The model was trained on data collected during 2023 and 2024, and its predictions for 2025 were validated against observed irrigation requirements. The proposed model enabled an average 12–18% reduction in total water application between 2023 and 2025, optimizing water use deprived of compromising crop yield. By merging satellite imagery, GIS data, and weather API information, this approach provides a cost-effective, scalable solution that enables precise, stage-specific irrigation scheduling. Cloud masking was executed by applying the built-in QA bands with the Fmask algorithm to eliminate cloud and cloud-shadow pixels in satellite imagery statistics. Time series were generated by compositing monthly median values to ensure consistency across images. The novelty of our study primarily focuses on its end-to-end integration framework, its application within semi-arid agronomic conditions, and its empirical validation and accuracy calculation over direct association of multi-source statistics with FAO-guided irrigation scheduling to support sustainable cotton cultivation. The quantification of irrigation capacity, determining how much water to apply, is identified as a focus for future research. Full article

The ultra-high-frequency (UHF) detection method is highly accurate and has a fault localization function. At present, most gas-insulated switchgear (GIS) installations are equipped with online UHF monitoring devices to detect partial discharges. In order to ensure the accuracy of the detection results, UHF sensors need to be verified regularly. UHF sensors used for online monitoring are usually installed at the handhole of the GIS and cannot be removed. Measuring the laboratory verification indexes (e.g., equivalent height, dynamic range, etc.) of the sensors directly is very difficult. However, it is easier to measure S₁₁ of the sensor for verification and S₂₁ between it and the neighboring sensors by injecting power signals. Accordingly, this paper proposes a degradation identification method for GIS UHF sensors using a cross-comparison of S-parameters. When sensor sensitivity decreases, S₁₁ increases while S₂₁ decreases, both serving as effective indicators of performance degradation. In this study, the equivalent S-parameter network and the variation mechanisms of S₁₁ and S₂₁ during sensor verification were first analyzed. Normal and typically degraded sensor models were then constructed and coupled in different GIS structures for electromagnetic simulation. The simulation and on-site verification results show that S₁₁ is mainly affected by the sensor’s intrinsic performance and installation conditions at the inspection port, whereas S₂₁ is predominantly influenced by sensor performance and the propagation characteristics of the GIS structure. Through cross-comparison of S₁₁ and S₂₁ at corresponding positions across three phases, sensor aging or failure can be effectively identified, enabling rapid on-site verification without removing the sensors. The proposed method was successfully validated on actual GIS equipment at the China Southern Power Grid Research Institute. It exhibits high accuracy, efficiency, and strong engineering applicability, enabling the early detection of degraded sensors and providing valuable support for condition assessment and maintenance decision-making in GIS online monitoring systems. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main technologies are discussed: the Laser Interferometer (LI), the Grating Interferometer (GI), and the Time Grating Sensor (TGS). The LI is widely regarded as the traceable benchmark for highest resolution; the GI has been developed into a compact and stable solution based on diffraction gratings; and the TGS has emerged as a new approach that converts spatial displacement into the time domain, offering strong resilience to environmental fluctuations. For each technique, the principles, recent progress, and representative systems from the past two decades are reviewed. Particular attention is given to the trade-offs between resolution, robustness, and scalability, which are decisive for practical deployment. The review concludes with a comparative analysis of performance indicators and a perspective on future directions, highlighting hybrid architectures and application-driven requirements in precision manufacturing and advanced instrumentation. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

To prevent or mitigate the negative impact of fires, spatial prediction maps of wildfires are created to identify susceptible locations and key factors that influence the occurrence of fires. This study uses artificial intelligence models, specifically machine learning (XGBoost) and deep learning (Kolmogorov-Arnold networks—KANs, and deep neural network—DNN), with data obtained from multi-sensor satellite imagery (MODIS, VIIRS, Sentinel-2, Landsat 8/9) for spatial modeling wildfires in Serbia (88,361 km²). Based on geographic information systems (GIS) and 199,598 wildfire samples, 16 quantitative variables (geomorphological, climatological, hydrological, vegetational, and anthropogenic) are presented, together with 3 synthesis maps and an integrated susceptibility map of the 3 applied models. The results show a varying percentage of Serbia’s very high vulnerability to wildfires (XGBoost = 11.5%; KAN = 14.8%; DNN = 15.2%; Ensemble = 12.7%). Among the applied models, the DNN achieved the highest predictive performance (Accuracy = 83.4%, ROC-AUC = 92.3%), followed by XGBoost and KANs, both of which also demonstrated strong predictive accuracy (ROC-AUC > 90%). These results confirm the robustness of deep and machine learning approaches for wildfire susceptibility mapping in Serbia. SHAP analysis determined that the most influential factors are elevation, air temperature, and humidity regime (precipitation, aridity, and series of consecutive dry/wet days). Full article

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

Wildfires pose severe threats to ecosystems and human settlements, making early detection and rapid response critical for minimizing damage. The adage—“You fight fire in the first second with a spoon of water, in the first minute with a bucket, and in the first hour with a truckload”—illustrates the importance of early intervention. Over recent decades, significant research efforts have been directed toward developing efficient systems capable of identifying wildfires in their initial stages, especially in remote forests and wildland–urban interfaces (WUIs). This review paper introduces the Special Issue of Fire and is dedicated to advanced approaches to wildfire detection, monitoring, and surveillance. It summarizes state-of-the-art technologies for smoke and flame detection, with a particular focus on their integration into broader wildfire management systems. Emphasis is placed on distinguishing wildfire monitoring (the passive collection of data using various sensors) from surveillance (active data analysis and action based on visual information). The paper is structured as follows: a historical and theoretical overview; a discussion of detection validation and available datasets; a review of current detection methods; integration with ICT tools and GIS systems; the identification of system gaps; and future directions and emerging technologies. Full article