Explainable Machine Learning for Geospatial Data Analysis

Auteur

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.

Texte du rabat

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.

Contenu

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.