Explainable Machine Learning for Geospatial Data Analysis
A Data-Centric Approach
$91.88
- Paperback
266 pages
- Release Date
22 June 2026
Summary
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 a…
Book Details
| ISBN-13: | 9781032503813 |
|---|---|
| ISBN-10: | 1032503815 |
| Author: | Courage Kamusoko |
| Publisher: | Taylor & Francis Ltd |
| Imprint: | CRC Press |
| Format: | Paperback |
| Number of Pages: | 266 |
| Release Date: | 22 June 2026 |
| Weight: | 440g |
| Dimensions: | 22mm x 156mm x 233mm |
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Courage Kamusoko
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.
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