GEOINFORMATION MODELS USING ARTIFICIAL INTELLIGENCE FOR AUTOMATING LAND USE CLASSIFICATION

The article discusses approaches to automated land use classification using geoinformation models and artificial intelligence methods. The relevance of combining satellite data, digital terrain models, and machine learning algorithms for cadastral analysis, environmental monitoring, and spatial planning is justified. A comparison of the effectiveness of different classification models, such as Random Forest, SVM, and CNN, is carried out using the example of territories with different land cover structures. A methodology for preparing input data, creating a feature space, training models, and constructing thematic land use maps is presented. The results obtained indicate the high accuracy and practical applicability of GIS and artificial intelligence for automating classification, detecting changes in land use, and forming analytical layers for management decision-making. The conclusions outline the prospects for the development of this area in the context of integration with web services, laser scanning data, multi-agent systems, and predictive models.