Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach, 1st Edition eBook
$39.99
. Explainable machine learning (XML), a subfield of AI, is focused on making complex AI models understandable to humans.
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About this eTextbook
. 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.
ISBNs for Explainable Machine Learning for Geospatial Data Analysis are 9781040252512, 1040252516 and the print ISBNs are 9781032503806, 1032503807. Additional ISBNs include 9781040252468, 9781003398257.
Publication details
- Author: Courage Kamusoko
- Publisher: CRC Press
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About this eTextbook
. 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.
ISBNs for Explainable Machine Learning for Geospatial Data Analysis are 9781040252512, 1040252516 and the print ISBNs are 9781032503806, 1032503807. Additional ISBNs include 9781040252468, 9781003398257.
Publication details
- Author: Courage Kamusoko
- Publisher: CRC Press
What you receive
- A licensed digital edition of this title, supplied as PDF or EPUB. The format is set by the publisher, so email us the ISBN before ordering and we will confirm which one it is.
- Delivered to the email address on your order as a secure download link, usually within minutes and always within 24 hours of payment.
- Download links do not expire and there is no cap on re-downloads. Readable on any computer, tablet or phone, online or offline.
- This is a digital edition. Nothing is printed, packed or posted, and there is no delivery charge.
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