Explainable Machine Learning for Geospatial Data Analysis: A Data Centric Approach
English | 2025 | ISBN: 1032503807 | 280 Pages | PDF EPUB (True) | 48 MB
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.
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