Artificial intelligence for visualizing land cover changes in central China - Lemenkova, Polina (Geografares, 2025)
Remote sensing (RS) data are essential sources of information for mapping landscape dynamics in urban areas. Algorithms of artificial intelligence (AI), including machine learning (ML), provide robust methods for processing RS data. This study used ML methods of GRASS GIS software for processing satellite images to analyse landscape changes in central China. The aim is to analyse landscape dynamics through detected land cover changes during 10 years with 2-year time gap. The workflow included Random Forest ML algorithm of image classification. Data included six Landsat 8-9 OLI/TIRS images taken in autumn in 2013, 2015, 2017, 2019, 2021 and 2023. The results indicated the expansion of the area of Wuhan city, which indicates the processes of urbanization and intensive land development. This article demonstrated the application of AI-enhanced approach in cartography to image analysis for analysis of landscape dynamics in central China.
Keywords: remote sensing; machine learning; image processing