Advances and Prospects in Machine Learning for GIS and Remote Sensing: A Comprehensive Review of Applications and Research Frontiers

Abstract

Machine learning (ML) has emerged as a transformative tool in the fields of Geographic Information Systems (GIS) and Remote Sensing (RS), enabling more accurate and efficient analysis of spatial data. This article provides an in-depth exploration of the various types of machines learning algorithms, including supervised, unsupervised, and reinforcement learning, and their specific applications in GIS and RS. The integration of ML in these fields has significantly enhanced capabilities in tasks such as land cover classification, crop mapping, and environmental monitoring. Despite its potential, the implementation of ML in GIS and RS faces several challenges, including data quality issues, computational complexities, and the need for domain-specific knowledge. This paper also examines the current status of ML usage in GIS and RS, identifying key trends and innovations. Finally, it outlines future directions for research, emphasizing the importance of developing more robust algorithms, improving data integration, and addressing the ethical implications of ML applications in spatial sciences.