DIRECTIONS OF APPLICATION OF ARTIFICIAL INTELLIGENCE TOOLS FOR COLLECTION AND PROCESSING OF GEOSPATIAL DATA IN MODERN UKRAINIAN REALITIES
DOI:
https://doi.org/10.17721/1728-2721.2025.94.6Keywords:
artificial intelligence, AI, geospatial data collection, geospatial data processing, software products, AI algorithms, AI methodsAbstract
Background. At the current stage of scientific and technological progress, amid general digitalization and a significant increase in geospatial data volumes, it has become essential to implement novel approaches for their collection, processing, analysis, and interpretation. In recent years, modern tools, software, and technological solutions have been increasingly employed for these purposes, with artificial intelligence (AI) playing a central role. In the context of geospatial data collection and processing, AI, referred to as GeoAI, can optimize the automation of routine processes, enhance the accuracy of results, and enable the detection of hidden patterns within large datasets.
Methods. The methodological foundation of this study is based on the synergy between geosciences and tools that automate the collection and processing of geospatial data by simulating human cognitive processes to accomplish the assigned tasks. To achieve the study’s objectives, the following methods were employed: systematic approach; analysis and synthesis; abstraction and concretization; induction and deduction; scientific experimentation; evaluation; scientific classification; and geoinformatics methods.
Results. The study identified that at the current stage of AI development, three groups of software products enable the intellectualization of task execution: specialized software with functionalities for automating various processes; generative AI tools that create diverse types of information resources; and agent-based AI tools, representing a new phase in the evolution of intelligent products. Together, these groups enhance the capabilities of geospatial data collection and processing, leveraging machine learning tools embedded within these software groups.
Practical examples of GeoAI in geospatial data collection and processing are presented, particularly in the development of digital terrain models using methods such as laser scanning and aerial imagery processing. A comparison of applied machine learning techniques, including k-means clustering and ISODATA, is also provided. The effectiveness of AI tools is assessed according to key criteria: speed and increased automation; accuracy and quality of geospatial data; and cost-effectiveness.
Conclusions. This study outlines directions for the application of AI tools in geospatial data collection and processing in modern Ukraine. These include the identification of necessary software product groups with intelligent functionalities, key algorithms and AI methods, main advantages, challenges, and opportunities for further enhancement of these tools. The results highlight the potential of GeoAI to significantly improve the efficiency, accuracy, and strategic value of geospatial data operations.
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