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Full Description
This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography.
Features
Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives
Covers a wide range of GeoAI applications and case studies in practice
Offers supplementary materials such as data, programming code, tools, and case studies
Discusses the recent developments of GeoAI methods and tools
Includes contributions written by top experts in cutting-edge GeoAI topics
This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.
Contents
Section 1: Historical Roots of GeoAI 1. Introduction to Geospatial Artificial Intelligence (GeoAI) 2. GeoAI's Thousands Years of History 3. Philosophical Foundations of GeoAI Section 2: GeoAI Methods 4. GeoAI Methodological Foundations: Deep Neural Networks and Knowledge Graphs 5. GeoAI for Spatial Image Processing 6. Spatial Representation Learning in GeoAI 7. Intelligent Spatial Prediction and Interpolation Methods 8. Heterogeneity-Aware Deep Learning in Space: Performance and Fairness 9. Explainability in GeoAI 10. Spatial Cross-Validation for GeoAI Section 3: GeoAI Applications 11. GeoAI for the Digitization of Historical Maps 12. Spatiotemporal AI for Transportation 13. GeoAI for Humanitarian Assistance 14. GeoAI for Disaster Response 15. GeoAI for Public Health 16. GeoAI for Agriculture 17. GeoAI for Urban Sensing Section 4: Perspectives for the Future of GeoAI 18. Reproducibility and Replicability in GeoAI 19. Privacy and Ethics in GeoAI 20. A Humanistic Future of GeoAI 21. (Geographic) Knowledge Graphs and Their Applications 22. Forward Thinking on GeoAI
