Land use and land cover change detection using AI and satellite imagery

Abstract:

This field of research has become a key issue in recent times; as the central aspect of monitoring the environment, resources, climate assessment, and urban planning. Conventional remote sensing methods, being fundamental, are frequently incapable of dealing with the workload of growing amounts of data, landscape complexity, and temporal dynamics with the emerging state-of-art satellite imaging. LULC change analysis has been revolutionized by the recent developments of Artificial Intelligence (AI), specifically machine learning and deep learning, which allow the processes to be more automated, accurate, and scalable. The methods that are based on AI can exploit the high-dimensional spectral data, multi-temporal data, and the heterogeneous streams of data in order to identify meaningful patterns, identify the most delicate changes, and categorize land features with utmost accuracy. This article discusses the fusion of AI and satellite images to detect LULC changes, including a methodology, data, algorithm, and use case. It underscores the ability of AI-based models to provide superiority in terms of flexibility, spatial generalizations, and real-time analysis as compared to conventional methods. The paper also analyzes issues like imbalance of the data, complexities in computations, and model explainability. The results highlight how AI can be transformative in facilitating dynamic, timely, and reliable LULC surveillance necessary to achieve sustainable development and environmental governance.