Machine Learning for Geospatial Analysis

Abstract

Historically, statistical techniques have been a major component of geographic analysis, which is the study of geographical phenomena using geographic data. But the emergence of machine learning (ML) has completely changed the area, making it possible to analyse intricate spatial relationships and glean insightful information from enormous geographical datasets. The foundations, methods, and applications of machine learning for geographic analysis are all covered in detail in this chapter. The authors start with a brief overview of machine learning and then move on to talk about its application to geographic analysis. Subsequently, they explore particular machine learning approaches that are frequently employed in the domain, such as decision trees, support vector machines (SVMs), K-means clustering, convolutional neural networks (CNNs), geostatistics, reinforcement learning, time series analysis, and anomaly detection. The authors wrap up by outlining the possibilities of machine learning for geospatial analysis in the future and provide resources for more research.