SUN’IY INTELLEKT YORDAMIDA FAZOVIY MA’LUMOTLARNI TAHLIL QILISH VA XARITAGA OLISHNI AVTOMATLASHTIRISH

This article highlights the possibilities of automating and simplifying mapping processes through the application of artificial intelligence technologies in the fields of Geographic Information Systems (GIS) and Remote Sensing (RS). Within the scope of the study, unsupervised machine learning algorithms were employed to identify land cover based on Sentinel-2 satellite imagery. The analyses were conducted using the Google Earth Engine cloud platform, where the K-means clustering algorithm was applied to automatically extract the main land cover classes. The results demonstrate that artificial intelligence approaches significantly reduce time and labor costs compared to classical GIS and RS methods, while enabling rapid and consistent mapping of large areas. In particular, under conditions of limited field data or when rapid analysis is required, unsupervised classification serves as an effective tool for GIS and RS specialists. This approach clearly demonstrates the practical importance of artificial intelligence technologies in mapping, environmental monitoring, and spatial planning processes.