Machine Learning for Geospatial Analysis: Enhancing Spatial Understanding and Decision-Making

Machine Learning for Geospatial Analysis: Enhancing Spatial Understanding and Decision-Making

Robin Rohit Vincent, E. Sakthivel, Meena Kumari, F. Nisha, A. Rohini
Copyright: © 2024 |Pages: 28
DOI: 10.4018/979-8-3693-6381-2.ch008
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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.
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Introduction

Combining geospatial analysis (H. Ouchra et al., 2023) with machine learning (M. Kumar et al., 2023) creates a novel synergy beyond conventional approaches to comprehending our spatial surroundings. The ability of machine learning to recognize intricate patterns and connections within data has made it an indispensable instrument for geospatial analysts. This integration makes complex analyses of satellite imagery, climatic data, and geographic data possible, providing previously complex insights to obtain via manual or rule-based methods.

Although machine learning holds great potential for geospatial analysis, there are still obstacles to overcome. A constant problem is guaranteeing machine learning models' interpretability and ethical usage, given the richness and volume of spatial data (J. Li et al., 2022). Despite this, the prospects brought about by this convergence of technology are revolutionary, opening up a whole new field for comprehending and handling the complexity of our globally interconnected environment. The future at the nexus of machine learning and geographic analysis presents intriguing opportunities for additional innovation, discovery, and refinement as we navigate this terrain.

Evolution of ML Within Geographic Analysis

The evolution is deeply intertwined with the advancements in both fields and the broader technological landscape. Here is a historical context of how ML has evolved within geographic analysis:

Early Years (1950s-1980s)

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    In the early years of ML, the focus was primarily on developing foundational concepts and algorithms. This period saw the emergence of key ideas such as perceptrons, neural networks, and decision trees.

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    Geographic analysis primarily relied on traditional statistical methods and manual interpretation of spatial data. GIS (Geographic Information Systems) emerged during this period, enabling spatial data storage, manipulation, and analysis.

Foundational Research (1980s-1990s)

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    During the 1980s and 1990s, ML research experienced a resurgence with the development more sophisticated algorithms and approaches. Among notable advancements are backpropagation for training neural networks, support vector machines (SVM), and ensemble methods like random forests.

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    Geographic analysis started incorporating computational techniques for spatial modeling and analysis. Remote sensing technologies became more prevalent, providing large volumes of spatial data for analysis.

Integration of ML and Geographic Analysis (2000s-2010s)

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    The 2000s marked a significant shift as ML techniques began to be integrated into geographic analysis. Researchers explored the application of ML algorithms for tasks such as land cover classification, object detection in satellite imagery, and spatial prediction.

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    Advances in computing power, data availability, and algorithmic sophistication enabled the scalability of ML approaches to handle large-scale geospatial datasets.

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    This period saw the emergence of interdisciplinary research efforts, with collaborations between geospatial scientists, computer scientists, and statisticians.

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