Integration of Remote Sensing and Artificial Intelligence Techniques in Land Cover Change Analysis

This study addresses the integration of remote sensing and artificial intelligence (AI) techniques in the analysis of land cover changes, with a focus on enhancing the effectiveness of spatial data analysis through AI. It reviews effective tools and platforms for implementing this integration and highlights the role of these modern technologies in detecting spatial changes in land cover and land use. The study includes an overview of the importance of remote sensing applications in land cover analysis, utilizing various sensing platforms within an analytical framework, while clarifying the concept of land cover classification and its applications.Additionally, the research discusses key components of artificial intelligence, such as machine learning and deep learning, and emphasizes the significance of Geographic Artificial Intelligence (GeoAI) in geographical studies. The study also outlines the procedural steps for analyzing satellite imagery using the Google Earth Engine platform and explains the image processing techniques applied in the analysis.The aim of this research is to enhance the techniques used in spatial change detection of land cover, improving the accuracy of monitoring environmental and urban transformations by integrating remote sensing with AI to detect subtle patterns of spatial change. Furthermore, the study contributes to forecasting future land cover changes and supports sustainable environmental resource planning.