Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.

Green infrastructure (GI) plays a crucial role in sustainable urban development, but effective mapping and analysis of such features requires a detailed understanding of the materials and state-of-the-art methods. This review presents the current landscape of green infrastructure mapping, focusing on the various sensors and image data, as well as the application of machine learning and deep learning techniques for classification or segmentation tasks. After finding articles with relevant keywords, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) method was used as a general workflow, but some parts were automated (e.g., screening) by using natural language processing and large language models. In total, this review analyzed 55 papers that included keywords related to GI mapping and provided materials and learning methods (i.e., machine or deep learning) essential for effective green infrastructure mapping. A shift towards deep learning methods can be observed in the mapping of GIs as 33 articles use various deep learning methods, while 22 articles use machine learning methods. In addition, this article presents a novel methodology for automated verification methods, demonstrating their potential effectiveness and highlighting areas for improvement.

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.

Understanding how built structures and urban vegetation jointly relate to population patterns is increasingly important for sustainable urban planning. This study used airborne LiDAR data to analyze three-dimensional urban morphology across Zagreb, Croatia, at neighborhood and district scales. A 1 m nDSM and classified rasters were used to derive canopy, building, height, volumetric, and built–vegetation balance metrics. Spatial clustering was assessed using Moran’s I, LISA, and Getis–Ord Gi*, while relationships with population density were evaluated using correlation and spatial regression models. At the neighborhood level, canopy cover ranged from 4.5% to 84.7%, while UMI ranged from 0.002 to 10.368. UMI showed significant spatial clustering (Moran’s I = 0.457, p = 0.001), with 24 high–high and 64 low–low clusters. Composite balance metrics outperformed individual vegetation or building indicators; logUMI provided the strongest performance for log-transformed population-density models, with SLM pseudo-R² = 0.903. Agreement assessment showed high consistency for canopy cover (R² = 0.997), while building height agreement was weaker for global datasets. Results indicate that transformed built–vegetation balance metrics provide useful complementary indicators for describing urban morphology and population density patterns.

Accurate information on intra-urban population distribution is essential for urban planning, infrastructure management, exposure assessment, and evidence-based decision-making. However, census data are usually available only for predefined administrative units and often lack the spatial detail required for fine-scale urban analyses. This study evaluates a top-down approach for estimating population distribution within the city of Zagreb, Croatia, using building footprints, three-dimensional building characteristics, OpenStreetMap (OSM) semantic information, and census population counts. The citywide population total was redistributed to LiDAR-derived roof and WSF-derived settlement polygons and aggregated to 17 city districts for validation. Two built-environment datasets were tested: locally derived LiDAR roof polygons and WSF-derived settlement polygons. Area-based scenarios were implemented for both datasets, while height-enhanced scenarios incorporated LiDAR-derived nDSM information. The WSF height-enhanced scenario therefore combined globally available WSF geometry with locally derived LiDAR height information and is referred to as WSF + nDSM. Results show that 3D models consistently outperformed 2D models. Total relative error (TRE) decreased from 15.5% to 11.8% for LiDAR and from 19.6% to 11.4% for WSF. OSM-based refinement further improved all scenarios, with the LiDAR 3D + OSM and WSF + nDSM + OSM scenarios achieving comparable overall performance (TRE = 9.0% for both at the city-district validation level). These findings demonstrate the value of combining vertical and semantic building information for more accurate urban population disaggregation.

Handheld personal laser scanning (PLS) systems are increasingly being tested in forest inventory as an efficient alternative to labor-intensive, time-consuming field-based methods. However, comparative evaluations across different PLS instrument classes and the influence of operator experience on estimation accuracy remain insufficiently explored. This study presents a controlled comparison of three handheld PLS instruments representing different performance and cost classes, namely professional-grade (high-end) and lower-grade (entry-level and open-source) systems, and evaluates the influence of operator experience on the accuracy of diameter at breast height (DBH) and tree height estimation. Data were collected in even-aged European beech stands using consistent acquisition and processing workflows. Tree attributes were independently estimated by operators with high, medium, and low experience and validated against reference measurements obtained from diameter tape and multi-scan terrestrial laser scanning. Accuracy was assessed using mean difference (bias) and root mean square error, and the effects of instrument type and operator experience were analyzed using one-way and two-factor repeated-measures ANOVA. Results show that instrument type is the dominant factor determining estimation accuracy. The high-end system produced the most accurate DBH and tree height estimates across all operator experience levels, whereas the entry-level and open-source systems yielded acceptable DBH accuracy but consistently underestimated tree height, particularly for taller trees. Operator experience had a secondary effect, improving DBH estimates when lower-grade instruments were used, but had little influence on tree height accuracy. Significant interaction effects indicate that operator influence depends on instrument class. These findings demonstrate that PLS can support operational forest inventory when instrument capabilities align with inventory objectives. High-end systems are currently optimal when reliable tree height estimation is required, whereas lower-grade systems may provide cost-effective solutions for inventories focused primarily on DBH.