Explainable machine learning for geospatial data analysis

• Verfasst von: Kamusoko, Courage [VerfasserIn]
• Titel: Explainable machine learning for geospatial data analysis
• Titelzusatz: a data-centric approach
• Verf.angabe: Courage Kamusoko
• Ausgabe: First edition
• Verlagsort: Boca Raton ; London ; New York
• Verlag: CRC Press, Taylor & Francis Group
• Jahr: 2025
• Umfang: 1 Online-Ressource (xiv, 265 Seiten)
• Illustrationen: Illustrationen
• Fussnoten: Includes bibliographical references and index
• ISBN: 978-1-003-39825-7
• DOI: doi:10.1201/9781003398257
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Kamusoko, Courage: Explainable machine learning for geospatial data analysis. - First edition. - Boca Raton, FL : CRC Press, 2025. - pages cm
• Sach-SW: TECHNOLOGY / Remote Sensing
• Sach-SW: TECHNOLOGY / Environmental Engineering & Technology
• K10plus-PPN: 1915388104
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

"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. It demonstrates the need for a data-centric explainable machine learning approach for obtaining 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. The author includes guidelines and code scripts (R, Python) valuable for practical readers"--