Integration of artificial intelligence and GIS technologies in geodesy
DOI:
https://doi.org/10.31548/zemleustriy2026.03.012Keywords:
artificial intelligence, GIS technologies, geodesy, geospatial data, machine learning, neural networks, digital maps, monitoring of territoriesAbstract
The integration of artificial intelligence and GIS technologies in geodesy is one of the most promising areas of development of modern geospatial research. Combining the capabilities of geoinformation systems with artificial intelligence algorithms allows you to automate the processes of collecting, processing, analyzing and visualizing spatial data. This contributes to increasing the accuracy of geodetic works, reducing the time of tasks and making more effective management decisions.
Modern geodetic projects involve working with large volumes of spatial information obtained with the help of satellite systems, unmanned aerial vehicles, laser scanning and remote sensing of the Earth. Traditional methods of processing such data often require significant time and labor resources. The use of artificial intelligence allows automatic detection of patterns, classification of objects and performance of data analysis with high speed and accuracy.
Of particular importance are machine learning and neural network technologies used for automatic object recognition in aerial and satellite imagery. Thanks to this, the creation of digital maps, updating geospatial databases and monitoring changes in the earth's surface is greatly simplified. Such approaches make it possible to quickly obtain up-to-date information about the development of territories, the state of land resources and natural processes.
GIS technologies provide effective storage, management and visualization of spatial data, while artificial intelligence expands the possibilities of their analysis and forecasting. The integration of these technologies allows the creation of intelligent geoinformation systems capable of automatically determining territorial development trends, assessing risks and predicting possible environmental changes.
An important direction of application of artificial intelligence in geodesy is the automation of cadastral works and land monitoring. Algorithms analyze large arrays of spatial data, detect changes in land boundaries, facts of unauthorized land use and other violations. This contributes to increasing the efficiency of land management and ensuring the accuracy of cadastral information.
The use of artificial intelligence to monitor engineering structures, transport infrastructure and natural sites offers significant prospects. By analyzing geodetic observation data in real time, intelligent systems are able to predict deformations, identify potentially dangerous areas and timely warn of possible emergency situations. This is particularly important in ensuring the security of critical infrastructure facilities.
The integration of artificial intelligence with GIS technologies is also actively used in urban planning and smart city concepts. Geospatial analysis allows optimizing traffic flows, planning the development of engineering networks and assessing the impact of urbanization on the environment. Thanks to this, the authorities receive tools for making informed decisions about the development of territories.
Despite numerous advantages, the introduction of artificial intelligence into geodesy requires solving issues of input data quality, information protection, and improvement of analysis algorithms. Further research is aimed at increasing the accuracy of machine learning models, developing automated geospatial data processing systems, and creating new intelligent services for the geodetic industry.
Thus, the integration of artificial intelligence and GIS technologies is an important stage in the digital transformation of geodesy. Their joint use makes it possible to significantly increase the efficiency of geodetic research, ensure high-quality analysis of spatial data and create new opportunities for territory management and development of modern infrastructure.
Received: 13.07.2026;
Accepted: 22.07.2026;
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