Explainable machine learning (XML), a subfield of AI, is focused on providing complex AI models that are understandable to humans. This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric explainable machine learning approach for obtaining new insights from geospatial data. It presents the opportunities, challenges, and gaps in the machine and deep learning approaches for geospatial data analysis and how they are applied to solve various environmental problems in land cover changes, and modelling forest canopy height and aboveground biomass. The author also includes guidelines and code scripts (R, Python) valuable for practical readers.FeaturesIncludes data-centric explainable machine learning (ML) approaches for geospatial data analysis. Provides the foundations and approaches to explainable ML and deep learning. Includes several case studies from urban land cover and forestry where existing explainable machine learning methods are applied. Identifies opportunities, challenges and gaps in data-centric explainable ML approaches for geospatial data analysis. Provides scripts in R and python to perform geospatial data analysis. This book is an essential resource for graduate students, researchers, and academics working and studying data science and machine learning. Geospatial data science professionals using GIS and remote sensing in environmental fields will also benefit from the new insights the author provides readers.

Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach PDF

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