Geographic information intelligent systems based on modern information technologies for digital terrain modelling

• Дата: 2025
• Автори: Vdovychenko, V.V., Pleskach, V.L.
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: PROBLEMS IN PROGRAMMING 2025
• Теми: artificial intelligence; computer vision; digital terrain modelling; deep learning; geographic infor mation system; digital elevation model; GeoAI; UDC 004.94
• Онлайн доступ: https://pp.isofts.kiev.ua/index.php/ojs1/article/view/860
• Назва журналу: Problems in programming

The article presents a comprehensive analysis of modern approaches to digital terrain modeling based on intel ligent geographic information systems and advanced information technologies. It highlights the evolution of terrain modeling methods — from traditional GIS technologies to the integration of artificial intelligence ap proaches (the GeoAI concept). The purpose of the study is to generalize the capabilities and advantages of ap plying artificial intelligence (machine learning, deep learning, and computer vision) to digital terrain modeling tasks and to identify prospects for the development of such intelligent systems. The study explores the application potential of supervised machine learning algorithms (decision trees, Random Forest, SVM) for landform classification and remote sensing data processing, as well as deep learning methods (CNN) for automatic pattern recognition and semantic terrain segmentation. Examples of intelligent GIS solu tions are provided in various applied fields: slope stability monitoring systems with landslide forecasting, flood risk assessment models, and military terrain planning systems with drone route optimization. The main results demonstrate that the implementation of AI technologies enables unprecedented detail and automation in terrain analysis. Modern geospatial intelligent systems are capable of integrating heterogeneous data sources (LiDAR scans, satellite imagery, drone data, ground sensors) and updating digital terrain models in real time, thereby supporting decision-making in land management and emergency response.The future development of GeoAI is defined by the continued integration of diverse data sources, the transition from static 3D models to dynamic 4D terrain representations, semantic enrichment of digital models (land scape ontologies, object detection), and their integration into the concept of territorial digital twins. It is con cluded that the synergy of traditional GIS tools with state-of-the-art artificial intelligence technologies is transforming the field of digital terrain modeling, opening new horizons for scientific research and practical applications.Problems in programming 2025; 3: 79-90