Courage Kamusoko – författare

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.FeaturesData-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.

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.FeaturesData-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.

This book examines the urban growth trends and patterns of various rapidly growing metropolitan regions in developing Asian and African nations from the perspective of geography. State-of-the-art geospatial tools and techniques, including geographic information system/science and remote sensing, were used to facilitate the analysis. In addition to the empirical results, the methodological approaches employed and discussed in this book showcase the potential of geospatial analysis, e.g. land-change modeling for improving our understanding of the trends and patterns of urban growth in Asia and Africa. Furthermore, given the complexity of the urban growth process across the world, issues raised in this book will contribute to the improvement of future geospatial analysis of urban growth in the developing regions. This book is written for researchers, academicians, practitioners, and graduate students. The inclusion of the origin and brief history of each of the selected metropolitan regions, including the analysis of their urban primacy, spatiotemporal patterns of urban land-use changes, driving forces of urban development, and implications for future sustainable development, makes the book an important reference for various related studies.

This book examines the urban growth trends and patterns of various rapidly growing metropolitan regions in developing Asian and African nations from the perspective of geography. State-of-the-art geospatial tools and techniques, including geographic information system/science and remote sensing, were used to facilitate the analysis. In addition to the empirical results, the methodological approaches employed and discussed in this book showcase the potential of geospatial analysis, e.g. land-change modeling for improving our understanding of the trends and patterns of urban growth in Asia and Africa. Furthermore, given the complexity of the urban growth process across the world, issues raised in this book will contribute to the improvement of future geospatial analysis of urban growth in the developing regions. This book is written for researchers, academicians, practitioners, and graduate students. The inclusion of the origin and brief history of each of the selected metropolitan regions, including the analysis of their urban primacy, spatiotemporal patterns of urban land-use changes, driving forces of urban development, and implications for future sustainable development, makes the book an important reference for various related studies.

This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing.

This book introduces remotely sensed image processing for urban areas using optical and synthetic aperture radar (SAR) data and assists students, researchers, and remote sensing practitioners who are interested in land cover mapping using such data.