Explainable Machine Learning for Geospatial Data Analysis : A Data Centric Approach by Courage Kamusoko (2024, Hardcover)

Great Book Prices Store (339688)

96.8% positive Feedback

Price:

US $163.28

Approximately£120.22

+ $19.99 postage

Estimated delivery Mon, 4 Aug - Mon, 18 Aug

Returns:

14 days return. Buyer pays for return postage. If you use an eBay delivery label, it will be deducted from your refund amount.

Condition:

New

"Explainable AI (XAI), a subfield of AI, is focused on providing complex AI models that are understandable to humans. This book highlights and explains details of machine learning models used in geospatial data analysis.

About this product

Product Identifiers

PublisherTaylor & Francis Group

ISBN-101032503807

ISBN-139781032503806

eBay Product ID (ePID)5072304195

Product Key Features

Number of Pages262 Pages

Publication NameExplainable Machine Learning for Geospatial Data Analysis : a Data Centric Approach

LanguageEnglish

SubjectEnvironmental / General, Remote Sensing & Geographic Information Systems

Publication Year2024

TypeTextbook

AuthorCourage Kamusoko

Subject AreaTechnology & Engineering

FormatHardcover

Dimensions

Item Length9.2 in

Item Width6.1 in

Additional Product Features

Intended AudienceCollege Audience

LCCN2024-029158

Dewey Edition23/eng/20241025

IllustratedYes

Dewey Decimal910.285/631

Table Of ContentPart 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.

SynopsisExplainable 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., 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.

LC Classification NumberG70.217.G46K3 2024