Machine learning can play a critical role in geospatial analysis, providing enhanced computing efficiency, flexibility, and scalability, improved predictive capabilities, complicated problem resolution, and information extraction from big datasets. Python has emerged as the predominant language for geospatial machine learning due to its user-friendly interface, extensive library support, and versatility. This chapter has explored a diverse ecosystem of Python libraries ranging from Geopandas, Fiona, Leafmap, Geemap, PySAL, and Shapely for geospatial data manipulation to Keras Spatial, TorchGeo, Scikit-learn, and TensorFlow for deep learning applications. Complementing this, it also explored a variety of QGIS Python plugins that enhance geospatial machine learning capabilities, including smart-map, cluster analysis, PyQGIS, ClusterPoints, AI vectorizer, mapflow, deepness, and many more, offering functionalities for digital mapping, clustering, and map segmentation.
TopIntroduction
Python is a broadly utilized general-purpose dynamic computer language. Python has been utilized in different disciplines due to its framework of modules, simple expansion, excellent graphics, object-oriented programming support, simple learning, fast development times, suitability for high-performance applications, and free download. For example, it is being used for computational physics (Borcherds, 2007), econometrics and statistics (Bilina & Lawford, 2012), and bioinformatics (Bassi, 2007).
In recent years, Python has become a powerhouse within the domain of geospatial data visualization and analysis visualization. Integrating Python with various open-source geospatial libraries and frameworks empowers experts and professionals to work with spatial information effectively for many tasks, such as point cloud analysis, real-time data processing, web-based mapping, clustering, and multivariate geophysical data analysis.
Furthermore, as highlighted by research findings, Python's dominance extends into machine learning applications. According to Anitha Elavarasi and Jayanthi (2022), the most commonly utilized programming languages for developing machine learning applications are Python by 57%, R by 31%, and Java by 17% data scientists. According to the Developer Nation survey, as shown in Figure 1, Python, with 16.9M developers, is the most popular language for Machine Learning (Dodd et al., 2023). This widespread adoption indicates Python`s suitability for high-performance applications and its appeal as a free and readily available language, cementing its position as a key player in modern data science and technology.
From the information mentioned above, it is clear that Python holds prominence as a popular programming language for machine-learning applications and is extensively employed in the development of geospatial machine-learning applications. Therefore, the primary aim of this chapter is to explore various avenues of geospatial machine-learning applications using the Python language. The chapter has been organized to address this objective: Section 2 provides the geospatial machine learning landscape. Section 3 outlines the key libraries, plugins, and cloud source solutions for spatial analysis using Python. The last section concludes the discussion.
Figure 1. Size of programming language communities
TopLiterature Review
Evidence-based decision-making in a variety of fields, including marketing, manufacturing, healthcare, education, financial modeling, and law enforcement, can be facilitated by machine learning (Jordan & Mitchell, 2015). Integrating machine learning with domain-specific knowledge facilitates its application across diverse fields, leading to novel scientific insights and discoveries (Roscher et al., 2020). For example, many areas of physics, such as particle physics, cosmology, quantum many-body physics, quantum computing, and chemical and material physics, can benefit from machine-learning techniques (Carleo et al., 2019). In archaeology, machine learning techniques can be employed to accurately classify large archaeological datasets to support identifying and categorizing archaeological features and artifacts (Bickler, 2021). Even machine learning techniques can be applied to the estimation of certain types of poverty (Hall et al., 2022), understanding weather and climate extreme events (S. Jiang et al., 2022), exploring the interpretability of materials science models (Oviedo et al., 2022), improve tedious and challenging aspects of teaching and learning (Jalil et al., 2019) and accurate and efficient disease prevention and diagnosis in clinical settings (Zhang & Sejdić, 2019).