Explainable Machine Learning for Geospatial Data Analysis

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.

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• Sistemas de informação geográfica (GIS) e observação remota
• Ciências da terra
• Aprendizagem mecânica
• Monitorização ambiental

Logistic Regression, Decision Trees, Support Vector Machines, Random Forests;Deep Convolutional Neural Networks (CNN);Geospatial Modeling, Landslide Susceptibility Modeling;Modeling Forest Canopy Height, Above-Ground Biomass (AGB);Land Cover Classification;R, Python