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

by Courage Kamusoko

Overview

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 environmental problems in land cover changes and in modeling forest canopy height and aboveground biomass density. The author also includes guidelines and code scripts (R, Python) valuable for practical readers.

Features

• Data-centric explainable machine learning (ML) approaches for geospatial data analysis.
• The foundations and approaches to explainable ML and deep learning.
• Several case studies from urban land cover and forestry where existing explainable machine learning methods are applied.
• Descriptions of the opportunities, challenges, and gaps in data-centric explainable ML approaches for geospatial data analysis.
• Scripts in R and python to perform geospatial data analysis, available upon request.

This book is an essential resource for graduate students, researchers, and academics working in and studying data science and machine learning, as well as geospatial data science professionals using GIS and remote sensing in environmental fields.

Product Details

ISBN-13:	9781032503806
Publisher:	CRC Press
Publication date:	12/06/2024
Pages:	280
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Courage Kamusoko is an independent geospatial consultant based in Japan. His expertise includes land-use/cover change modeling and the design and implementation of geospatial database management systems. His primary research involves analyses of remotely sensed images, land-use/cover modeling, modeling aboveground biomass, machine learning, and deep learning. In addition to his focus on geospatial research and consultancy, he has dedicated time to teaching practical machine learning for geospatial data analysis and modeling.

Table of Contents

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.