Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach [Hardcover]

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 analysis and how they are applied to solve various environmental problems from forestry to climate change.

Part I: Introduction.  1. Challenges and Opportunities.  Part II: Foundations.  2. An Introduction to Explainable Machine Learning.  3. Approaches to Explainable Machine Learning.  4. Approaches to Explainable Deep Learning.  5. Landslide Susceptibility Modeling Using a Logistic Regression Model.  Part III: Techniques and Applications.  6.  Urban Land Cover Classification Using Earth Observation (EO) Data and Machine Learning Models.  7. Modeling Forest Canopy Height Using Earth Observation (EO) Data and Machine Learning Models.  8. Modeling Aboveground Biomass Density Using Earth Observation (EO) Data and Machine Learning Models.  9. Explainable Deep Learning for Mapping Building Footprints Using High-Resolution Imagery.  10. Towards Explainable AI and Data-Centric Approaches for Geospatial Data Analysis.  11. Appendix.

Explainable machine learning (XML), a subfield of AI, is focused on making complex AI models 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 to obtain 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 enl3¼