In the current context of renewable energy development, the most important task is to minimize the environmental impact of wind farms (WPPs) on the environment. The use of geographic information systems (GIS) allows for effective analysis and modeling of the impact of WPP placement, taking into account environmental, landscape, climatic and social factors. The article considers the use of geographic information systems (GIS) to assess the environmental impact of the location of a wind farm (WPP) on the territory of the "Polonyna Runa" tract of the Turye-Remetivka territorial community. The study involves the collection and processing of spatial data, including relief, wind speed and direction, the presence of protected areas and habitats of sensitive flora and fauna. GIS will be used to analyze visual and noise impacts, assess risks to biodiversity and public health, and model alternative siting options. The assessment results contribute to making informed decisions regarding the choice of a location for wind power plants, reducing negative environmental impact and ensuring a balance between the development of green energy and the preservation of the natural environment.

The paper reconceptualizes the boundary in contemporary land management as an institutional–regime category that transcends the geometric view of a dividing line. Drawing on comparative-historical and institutional analysis, deontic modeling, and formal ontologization via LADM, it substantiates a shift from a material-geometric to a regime-based ontology of space, where the boundary operates as an event-relation constituting rights, restrictions, liabilities, and rent flows. Four structural levels—material, legal, informational, and ecological—are identified, and their supervenient couplings are explicated, showing how procedural and data validity causally ground the legitimacy of spatial regimes. The paper formulates principles of boundary ontological design (constitutiveness of procedure, multimodality, adaptability, quality metrics, compatibility and priorities, value transparency) enabling the integration of legal norms, spatial data, and land-value governance mechanisms. Practical relevance includes a transition from a “registry of lines” to a dynamic regime model of space for cadastral and planning systems, improving decision quality, reducing conflicts, and enabling transparent value sharing. The study positions itself as a contribution to the formation of land management theory as a foundational discipline and outlines follow-up work on regime formalization, GeoBIM integration, and legitimacy assessment procedures.

As part of the study, the available geospatial and attributive data on dangerous burials were structured, and a conceptual model of a geospatial database was developed, which ensures the accumulation, structuring, and systematization of data. The model is built using UML notation and is presented in the form of a class diagram that reflects the main structural elements of the database and their attributes.

The article considers modern requirements for the development of working land management projects for the removal and transfer of the fertile soil layer in accordance with the regulatory framework of Ukraine. The main tasks of such projects are outlined, including ensuring the rational use and preservation of soil fertility, minimizing the negative consequences of economic activity and restoring disturbed lands. An analysis of key indicators that determine the feasibility of removing and storing soil layers is carried out: humus content, acidity, exchangeable sodium content and granulometric composition. It is shown that compliance with these parameters is an important factor in environmental safety and sustainable development of agricultural production.

Sustainable land use cannot be achieved without deliberate efforts to preserve and restore soil fertility, with the humus status serving as its key indicator. The challenge of maintaining a stable humus balance has become particularly pressing under the dominance of short-rotation crop sequences in the agricultural practice of the Southern Steppe of Ukraine. The absence of systematic monitoring of soil organic matter complicates the prediction of agroecosystem productivity and threatens the degradation of chernozems, the country’s most valuable agricultural resource. This study aimed to assess the performance of different short-rotation crop rotations under conditions of complete fertilizer exclusion, to quantify their effects on the humus balance, and to identify the most suitable model for long-term land use. The methodological framework was based on humus balance calculations in a five-field rotation with winter rapeseed and sunflower, conducted during 2020–2024. The results indicated that technical crops generated a negative humus balance (–0.28 t/ha and –0.77 t/ha, respectively), with the most severe deficit observed after bare fallow (–1.46 t/ha). In contrast, cereals contributed to partial restoration of humus reserves (up to +0.21 t/ha and +0.08 t/ha), while the highest positive effect was obtained from green fallow (+1.14 t/ha). The overall balance per rotation amounted to –2.22 t/ha in the first sequence and +0.38 t/ha in the second. These findings highlight the importance of incorporating green fallow into crop rotations as a natural source of organic matter, essential for sustaining chernozem fertility and ensuring the resilience of agricultural production in the Southern Steppe of Ukraine.

Rapid metropolitan growth is reshaping agrarian viability in peri-urban regions. Using the Kyiv agglomeration as a data-rich testbed, this study couples a modular UAV–AI decision-support workflow with empirical constraints emerging from land use, labour markets, logistics, and land rents. Regional evidence shows agricultural land has been squeezed to 0.21% of the area (˜0.18 k ha), against >54% green zones; wage differentials (16,500 UAH food processing vs 14,000 UAH agriculture) and transport costs of 20–250 UAH/km undermine farm margins and labour retention, while prime-zone rents up to 25 million UAH/ha/year intensify conversion pressure (Kyiv & oblast baseline tables and figures). These structural frictions motivate digital agronomy that is explicitly policy- and cost-aware. We therefore prototype a decision-support workflow that fuses UAV/satellite imagery, in-field IoT, and historical climate/crop data with administrative-economic layers (rents, wage gradients, haulage costs). The system translates multisource inputs into actionable stress detection, irrigation timing, and input allocation recommendations. At the same time, a logistics module evaluates route/vehicle choices under peri-urban cost profiles—a stakeholder-co-design process (farmers, processors, and planners) anchors usability and transferability. We report (1) the peri-urban baseline for Kyiv (land, wages, logistics, enterprise distribution), (2) the architecture of the UAV–AI workflow and integration points with farm CRMs and public agri-data, and (3) an evaluation framework linking agronomic KPIs to spatial-economic constraints for resilient adoption. The approach is designed for cross-border replication (Ukraine - Germany) and to inform respectable policy outputs on digital land management and peri-urban agrifood resilience.

The article examines the impact of artificial intelligence neural network algorithms on the process of photogrammetric processing of digital images and the formation of dense point clouds. It is noted that the integration of machine learning algorithms, in particular deep learning, allows you to automate key processing stages, increase the accuracy of object classification, optimize geometric correction of images and improve the quality of final geospatial products in the form of an orthophotomap of the area. The main stages of the photogrammetric process are described: from collecting primary data to forming orthophotomaps and three-dimensional terrain models. Particular attention is paid to the role of the neural network in improving the density of the point cloud due to the correct interpretation of points on digital images, object classification and reducing the influence of the human factor. The challenges associated with the need for large volumes of high-quality training data are identified. The prospects for the application of AI neural networks in photogrammetry and the need for further research in this direction are substantiated.

The regulatory and legal principles of designing water protection zones and their role in protecting the natural environment, determining the boundaries of coastal protection strips and the influence of relief on the size of restriction zones are analyzed. The territory of the Kamianopotokivska territorial community in the Kremenchuk district of the Poltava region is studied. The water bodies in the study area were classified, and the normative values of coastal protection strips were determined based on their sizes, in accordance with current legislation requirements. Based on open data on the relief of the territory, slopes were calculated and doubling of coastal protection strips within slopes exceeding 3 degrees was provided. A geospatial dynamic model for designing water protection zones, taking into account legislative norms and terrain and their impact on activities, has been developed. The model was created and tested using the Model Builder software module in ArcGIS Pro. As a result of the study, the geospatial model was applied to the collected input data and water protection zones were designed within the study area. The territories were identified and analyzed, the ownership rights of which are encumbered in accordance with the designed water protection zones. This model is dynamic and suitable for use for production purposes at the local level (within territorial communities).