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Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach - Couverture souple
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l'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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Explainable Machine Learning for Geospatial Data Analysis (Paperback)
Edité par
Taylor & Francis Ltd, London, 2026
ISBN 10 : 1032503815
ISBN 13 : 9781032503813
Neuf
Paperback
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. 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 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781032503813
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Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach
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Explainable Machine Learning for Geospatial Data Analysis (Paperback)
Edité par
Taylor & Francis Ltd, London, 2026
ISBN 10 : 1032503815
ISBN 13 : 9781032503813
Neuf
Paperback
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. 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 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9781032503813
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Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach
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Explainable Machine Learning for Geospatial Data Analysis (Paperback)
Edité par
Taylor & Francis Ltd, London, 2026
ISBN 10 : 1032503815
ISBN 13 : 9781032503813
Neuf
Paperback
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. 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 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781032503813
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Explainable Machine Learning For Geospatial Data Analysis: A Data-Centric Approach
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Explainable Machine Learning for Geospatial Data Analysis
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Etat : New. 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 ana. N° de réf. du vendeur 3233452738
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