Search Results (4,169)

Operational agricultural monitoring in the Central European lowlands requires timely parcel boundaries; however, unmarked field edges produce minimal spectral contrast in single-date imagery. Previous works demonstrated that harmonic NDVI encoding enables zero-shot field delineation using foundational models, but the influence of the spectral index choice on temporal boundaries remained unquantified. This study systematically evaluates eleven vegetation indices—NDVI, GNDVI, NDRE, EVI, EVI2, SAVI, MSAVI, NDWI, CIg, CIre, and NDYVI—within a fixed harmonic phenology encoding pipeline. A one-year PlanetScope time series (15 × 15 km, Slavonija, Croatia) was decomposed via annual sinusoidal regression to extract per-pixel phase, amplitude, and mean parameters. These harmonic descriptors were mapped to HSV colour channels and segmented using the Segment Anything Model without fine-tuning. Official agricultural parcels (PAAFRD, 2025) provided ground truth for pixel-wise, object-wise, and size-stratified evaluation. Performance stratified into three tiers based on object-wise metrics. Soil-adjusted and enhanced-greenness indices (MSAVI, EVI, EVI2, and SAVI) achieved F1 = 0.51–0.52, and mIoU = 0.70–0.71, statistically outperforming standard ratio formulations (NDVI: F1 = 0.49) and chlorophyll indices (CIg, CIre: F1 = 0.45–0.47). Pixel-wise scores remained compressed (F1 > 0.88 across all indices), indicating consistent interior coverage but index-dependent boundary precision. Error analysis revealed scale-dependent patterns: merging dominated small parcels (<10,000 m²), while fragmentation increased with parcel size. Results demonstrate that spectral formulation is a systematic design factor in phenology-based delineation, with soil background correction and dynamic range compression improving seasonal trajectory separability. The harmonic parameters generated by this framework provide feature-ready input for crop classification, suggesting that integrated boundary extraction and crop mapping workflows merit further investigation. Full article

Melissopalynological analysis is essential for determining the botanical origin of honey, corbicular pollen and bee bread, as well as detecting adulteration. However, it traditionally relies on labor-intensive and subjective manual pollen identification. As a proof-of-concept preceding full honey analysis, this study evaluates artificial intelligence methods for automated pollen grain recognition under controlled conditions. Hazel (Corylus avellana L.) and dandelion (Taraxacum officinale F.H. Wigg.) were used as model taxa to validate the proposed approach before its application to real varietal honey samples. This study introduces a novel three-stage pipeline that decouples object detection from feature extraction, utilizing YOLOv12m for region-of-interest generation and, for the first time in melissopalynology, DINOv3 ConvNeXt-B for deep feature representation. Microscopic images acquired at 400× magnification yielded 2498 dandelion and 1941 hazel pollen grains. The detector achieved an mAP@0.5 of 0.936 with an F1 score of 0.88, while the classifier reached 98.1% accuracy with good class separability (Silhouette coefficient: 0.407). The primary technical contribution is the systematic optimization of the detection-to-classification interface. Context-aware bounding box expansion (12%) and an optimized IoU-NMS threshold (0.65) significantly improve the stability of morphological feature extraction, as confirmed by ablation studies. Computational cost reporting further supports reproducible, deployment-oriented comparison. The results confirm the feasibility of this AI-based framework as an intermediate step toward automated melissopalynological analysis, with future work focusing on standardized microscopy protocols and expanded pollen databases for varietal honey authentication. Full article

Climate change is altering the frequency, duration, and seasonality of low flows, which are critical for water availability, ecosystem functioning, and river management. Low-flow characteristics, defining the minimum, often seasonal, flow levels in rivers or streams primarily fed by groundwater, snow or glacier melt, or lake drainage, are essential for assessing hydrological droughts and water resource vulnerability. In the Upper Vistula River Basin, variable precipitation and rising air temperatures increase the risk of droughts, impacting both natural systems and human water use. This study analyzed long-term trends in annual low flows and associated parameters, including drought frequency, duration, and deficit volume, across 41 small- and medium-sized catchments. Two datasets were considered: 25 stations with 58-year daily discharge records (1961–2019) and 41 stations with 38-year records (1981–2019). Low flows were identified using the threshold level method (TLM) at 70% and 90% exceedance (FDC70 and FDC90). Trends were assessed with the Mann–Kendall test, and spatial drought patterns were mapped to evaluate regional variability. Deep and shallow low flows occurred at all analyzed cross-sections. For the period 1961–2019, deep low flows (FDC90) occurred almost annually in 18 of the 25 cross-sections since 2012. Statistically significant increasing trends in deep low-flow parameters were detected in five cross-sections for 1961–2019 and in seven cross-sections for 1981–2019. Shallow low flows (FDC70) occurred in all sections; four rivers exhibited annual shallow droughts during 1961–2019, whereas 12 rivers showed annual events in 1981–2019. Summer droughts predominated over winter events, reflecting enhanced evapotranspiration and higher seasonal water demand. These findings highlight the relevance of analyzing low-flow parameters for understanding hydrological droughts. Such information can support water resource management, planning, and ecosystem protection under variable climatic conditions. Full article

The aim of the study was to analyze the impact of the COVID-19 pandemic on water consumption in tourist destinations. The city of Ostróda, located in north-eastern Poland in the Warmian-Masurian Province, was selected as the subject of the study. The data for the analysis was obtained from Przedsiębiorstwo Wodociągów i Kanalizacji Ostróda sp. z o.o. (Ostróda Water Supply and Sewerage Company). It included monthly water consumption in individual consumer groups and the hourly volume of water pumped into the network. The following periods were subjected to a comparative analysis: before (1 January 2018–31 March 2020), during the pandemic (1 April 2020–31 May 2022), and in the phase of easing restrictions (1 June 2022–31 December 2023). Water consumption was analyzed using parametric and non-parametric tests in individual customer groups, with a distinction made between the following sectors: households, non-production services, food industry, wholesale, production services, and other industries. In addition, an analysis was carried out of the impact of the COVID-19 pandemic on the daily and hourly unevenness of water supply to the water supply network in individual research periods. Based on the analyses carried out, it was found that the impact of the pandemic on total water consumption in the water supply system was insignificant, but varied greatly among individual consumer groups. However, the pandemic did not have a major impact on the values of the coefficients of unevenness of water supply to the network or on the volume and times of peak supply. Based on the analyses, it was found that the COVID-19 pandemic did not significantly affect water consumption in households, but significant changes were observed in the industrial and service sectors (η² = 0.26–0.37; ε² = 0.36–0.52). There was no significant impact of the pandemic on the values of water supply irregularity coefficients or the size and hours of water supply peaks. Full article

The need for Structural Health Monitoring is evident in order to ensure the safety of civil infrastructure. The goal of vibration monitoring is to derive the eigenfrequencies, mode shapes and damping of a structure. A change in the eigenfrequency over time can indicate deterioration or damage in a structure. The amplitude can be used to calculate the damping ratio. As the damping ratio is amplitude-dependent, it is important to correctly determine the amplitude values. This study focuses on the amplitude correctness of high-rate Global Navigation Satellite System (GNSS) receiver data. In an experiment with controlled oscillations with a shaker and a Laser Triangulation Sensor (LTS) as a reference, the vibration amplitudes derived by GNSS measurements were analyzed, using time-frequency techniques like Short Time Fourier Transform (STFT) and Wavelet Transform (WT). We demonstrate that vibrations in the millimeter range can be derived from the measurements of satellites orbiting 20,000 km above Earth. However, the amplitudes of the determined frequencies show systematic errors up to 60% when compared to independent reference measurements. We introduce a correction method to reduce this error by applying a frequency-dependent correction function. Full article

Railway corridors create persistent linear habitats embedded within intensively managed agricultural landscapes and can simultaneously support native biodiversity and facilitate the spread of undesirable taxa. We evaluated vegetation composition across five habitat types associated with railway line no. 250 (Havlíčkův Brod–Tišnov, Czech Republic): railway yard, railway embankment, railway land, field margin, and adjacent arable land. Vegetation was recorded using phytosociological relevés (10 m²) at four localities during three surveys in the 2021 growing season. In total, 83 plant taxa were identified, with pronounced differences among habitat types. Species richness and vegetation structure were highest in railway embankments, railway land, and field margins, whereas the railway yard and arable land exhibited lower diversity consistent with high disturbance intensity and substrate constraints. Canonical correspondence analysis distinguished habitat-affinity assemblages, indicating strong habitat filtering along the railway–agriculture gradient. Classification by origin and invasion status showed that non-native and invasive taxa were concentrated predominantly in railway embankments and adjacent habitats, suggesting elevated propagule pressure and potential spread into surrounding farmland. Colonization success (ICS) and colonization potential (ICP) indices indicated that railway-associated habitats can host taxa with high establishment capacity, contributing to successional stability within the corridor. These findings highlight railways as multifunctional elements of agricultural landscapes that require integrated vegetation management to balance biodiversity benefits with operational safety and invasive species risk. Full article

Multi-view 3D reconstruction is essential for smart city, supporting applications such as smart city planning and autonomous navigation. While traditional reconstruction pipelines and recent neural implicit methods, such as NeRF, achieve high visual fidelity, they often struggle with geometric accuracy and sparse-view scenarios. To address this challenge, we present DiffLiGS, a novel multi-modal 3D reconstruction framework that integrates LiDAR point clouds and LiDAR-guided diffusion-based priors into the 3D Gaussian Splatting (3DGS) pipeline, enabling high-fidelity and geometrically accurate models. Our method first densifies sparse LiDAR depths using a diffusion model and refines them through multi-view geometric constraints, producing dense LiDAR depth maps that provide robust supervision for 3DGS optimization. Leveraging these dense depth maps, we guide a Stable Video Diffusion model to synthesize novel view images, which are incorporated into training to enhance reconstruction completeness and visual realism. By jointly fusing rich appearance cues from multi-view images with precise LiDAR-derived geometry and diffusion priors, DiffLiGS achieves unified, geometry-aware 3D scene representations. Our extensive experiments demonstrate that our approach significantly improves both geometric accuracy and rendering quality compared to existing 3D reconstruction methods, enabling real-time, high-precision modeling of complex urban environments. Full article

The issue of emergency facility location is a long-term strategic issue, and the complexity and diversity of the decision-making environment force decision-makers to focus on multiple objectives when making location decisions. We develop a multi-objective optimization system centered on cost-effectiveness, service balance, and fairness, targeting three core objectives: minimizing total costs, minimizing differences in service quality among demand points, and minimizing material shortage gaps between demand points. To address the issue of limited facility service capacity induced by material shortages, we establish a multi-objective optimization model for the reliable location of emergency facilities. By combining the model’s characteristics with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and an elite retention strategy, the Pareto frontier solution set of the multi-objective model is obtained, and the model’s feasibility is verified through various examples of different scales. Finally, sensitivity analysis was conducted on the reliability location model of emergency facilities under different disruption risks using the control variable method, and the topology structure of the reliability location allocation network for emergency facilities under different disruption situations is obtained. The research findings provide decision-makers with actionable references and technical support for selecting reliable locations for emergency facilities amid disruption risks. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

Spatial technologies play a key role in documenting and analyzing landscape components. The Landscape Information Model (LIM), deriving from the Building Information Model (BIM), is a digital representation of a landscape, which should support planning, design, management, and analysis throughout a landscape’s lifecycle. In the literature, the applications of BIM technology in landscape planning focuses on the design and the construction of 3D and 5D LIMs. The aim of this paper is to develop the concept of 7D LIMs for the purposes of managing greenery based on the example of the university campus and model implementation based on BIM-GIS technology. The specific objective is to develop the UML diagrams of the model that would be dedicated to the needs of the unit responsible for maintaining the university’s infrastructure. The source of data was a point cloud obtained by laser scanning, which was then processed to map the terrain, small architectural objects, and infrastructure in the Revit 2024 software. The developed method indicated the value of modern technologies in landscape processes and their potential use in public institutions. The proposed diagrams that describe the semantics of landscape forms and greenery maintenance activities can be developed by adding further ontological aspects of the landscape model. Full article

Real-time, precise monitoring of the number and distribution of indoor personnel is crucial for building safety management, operational optimization, and personnel scheduling. However, narrow entrances and high-density passageways often lead to missed detections, false positives, and tracking failures in pedestrian detection, thereby reducing cross-line counting accuracy. Additionally, edge devices deployed in practical scenarios frequently process multiple video streams simultaneously, resulting in computational resource constraints. To address these challenges, this paper proposes a lightweight, enhanced multi-object pedestrian tracking and counting method tailored for indoor scenarios by optimizing deep learning models. Firstly, modular optimizations are applied to the YOLOv8n model to construct a more lightweight detector, RL_YOLOv8, reducing computational overhead while maintaining accuracy. Secondly, correlated pedestrian auxiliary prediction and pedestrian position change constraints are employed to mitigate ID switching, tracking interruptions, and trajectory jumps in dense scenes. Finally, a buffer zone auxiliary counting strategy is designed to further reduce missed detections of pedestrians crossing lines. Experimental results demonstrate that compared to the original detection-and-tracking-based line-crossing counting method, the improved approach effectively enhances counting accuracy and real-time performance, better meeting the requirements of practical intelligent security and crowd monitoring systems. Full article

Urban blue–green infrastructure (BGI), comprising vegetation and aquatic elements, is fundamental to city resilience and climate adaptation. Effective BGI management necessitates high-resolution, spatially accurate data for which Unmanned Aerial Vehicles (UAVs) have emerged as versatile monitoring tools. This study provides a critical synthesis and analytical evaluation of UAV-based technologies for BGI management from 2018 to 2025. Following a PRISMA-guided methodology, the review evaluates dominant research themes, sensor technologies (RGB, multispectral, thermal, LiDAR, and water and air quality sensors), and analytical methods. Departing from traditional descriptive reviews, this study appraises the operational maturity of these technologies using an adapted Technology Readiness Level (TRL) framework. The analysis identifies a significant “maturity gap” between standardized structural mapping (TRL 9) and experimental functional assessments of environmental conditions (TRL 4–6). Notably, the article includes a detailed analysis of specific UAV platforms and sensors, providing specifications of technological capabilities. By identifying critical technical, regulatory, and economic bottlenecks, this review provides a robust, evidence-based foundation for the deployment of drones in enhancing urban resilience and sustainable environmental governance. Full article

This paper presents a methodological framework for integrating UAV-based photogrammetry and GIS technologies to generate a high-accuracy digital elevation model (DEM) for urban land-use planning. The study was conducted in an urbanized area characterized by heterogeneous topography, mixed vegetation cover, and fragmented land use, which complicate high-resolution terrain modeling. UAV surveys were performed using multiple photogrammetric blocks with centimeter-level ground sample distance and a dense ground control network supported by geoid-based height corrections. The resulting DEM was independently validated using control points derived from large-scale topographic data. The achieved vertical accuracy (RMSE ≈ 0.25 m) confirms the applicability of UAV-derived DEMs for large-scale mapping (1:1000–1:2000) and urban spatial analysis. Unlike studies focused on runoff simulation, this work emphasizes the accuracy-controlled generation and validation of DEMs as a primary spatial dataset for urban planning applications. The results demonstrate that DEM accuracy depends strongly on flight planning, ground control distribution, and hybrid automatic–manual point cloud refinement. Full article

Attenuated Total Reflection–Fourier Transform Infrared (ATR-FTIR) spectroscopy and Inductively Coupled Plasma–Optical Emission Spectrometry (ICP-OES) are widely used to investigate the chemical structure and elemental composition of materials. However, the combined application of both methods to examine scale deposits in the water supply network has not yet been explored. In this study, scale deposits collected from the inlets of six pipes (steel, cast iron, lead, wooden) were analysed using both techniques. The application of ATR-FTIR and ICP-OES enabled the identification of mineral phases, organics, and structural differences between individual scale layers. Iron oxyhydroxides, together with silica and aluminosilicates, dominated most samples, whereas shower faucet deposit was primarily composed of carbonates and stearates. The combined analytical approach helped to avoid misinterpretation of FTIR data: although the spectrum of lead pipe deposit resembled hydrated lead carbonates, ICP-OES revealed only trace amounts of lead. Differences in crystallinity between successive layers allowed the reconstruction of the deposition process within the pipes. Poorly crystalline iron oxyhydroxides and silica occurred near pipe walls, while more crystalline phases developed closer to the water interface. These results demonstrate that combining ATR-FTIR and ICP-OES provides a reliable framework for interpreting scale deposit composition and formation in water distribution systems. Full article

This study investigates an eco-friendly rapid-setting concrete developed for emergency repair and accelerated post-disaster reconstruction. The proposed material concept combines a low-emission multicomponent cement, CEM V/A (S-V) 42.5 N-LH/HSR/NA, with a hybrid aggregate skeleton composed of crushed granite and waste soda–lime glass, as well as a waste-derived silicate additive system based on aqueous sodium silicate, glass dust and glass powder. One reference mixture (R) and five modified mixtures (M1–M5) were designed to assess the effects of partial replacement of natural aggregate by glass aggregate and of the dosage of the silicate-based additive system on concrete performance. The experimental programme included setting time, compressive strength, splitting tensile strength, water absorption, freeze–thaw resistance and microstructural observations. Among the modified concretes, the mixture containing 5 vol.% glass aggregate (M1) showed the most favourable mechanical performance after 28 days, reaching a compressive strength of 95.1 ± 2.4 MPa and a splitting tensile strength of 4.82 ± 0.29 MPa, compared with 45.5 ± 0.8 MPa and 2.18 ± 0.11 MPa, respectively, for the reference concrete. Higher glass contents reduced strength relative to M1, but the modified mixtures still maintained satisfactory performance. The silicate-based system significantly affected setting behaviour; in mixture M5, the initial and final setting times were reduced from 380 ± 5 min and 497 ± 5 min to 213 ± 5 min and 307 ± 5 min, respectively. The results show that the combined use of CEM V cement, waste glass and silicate-based waste-derived additives can produce concretes with rapid-setting, high strength and satisfactory durability-related properties. The developed material may therefore be considered a promising solution for selected rapid-repair and reconstruction applications, particularly in lightly reinforced or unreinforced concrete elements requiring fast restoration of functionality. Full article