satellite imagery

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.

Objective. The aim of this study is to identify potentially abandoned agricultural lands within the Vyhoda territorial community of Ivano-Frankivsk region through multi-criteria analysis based on Sentinel-2 satellite imagery, vegetation indices, and topographic and infrastructural parameters. Methodology. An  adapted multi-criteria spatial modeling approach was proposed to detect potentially abandoned agricultural lands. The study employed multispectral Sentinel-2 satellite images acquired in August 2024.