geographic information systems

The integration of artificial intelligence algorithms in agriculture has been studied to support efficient and sustainable agricultural production through the use of machine learning technologies, automated data collection, and analysis of large volumes of geospatial data to improve land resource management using geographic information systems. The article analyzes the prospects of applying artificial intelligence (AI) methods in the QGIS geographic information system for spatial analysis of agricultural land use.

The aim of the research is to develop fuzzy impact models of the natural and anthropogenic influence, which allows to integrate different physical factors, which makes it possible to bring them to a single environmental assessment system and comparison of different assessed areas. Methodology. The basis of the proposed modeling is a traditional approach on the development of such models, which includes conceptual, logical and physical modeling levels.

The article researches the principles of creation and assignment of a unique identifier inspireId to objects of classes in the topographic database (TDB), which is developed on the basis of the concept of model-driven architecture. The issue of automatic generation of a unique identifier inspireId and the rules of its life-cycle is researched.

This article aims to identify approaches in generating cartographic and attribute information in a GIS environment in order to implement the landscape approach in land management through the example of a land use area formed based on land plots (shares). In so doing, it is important to take into account the topsoil properties and relief features of the area under land management.