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.
Author(s): Yingjie Hu, Song Gao
Publisher: CRC Press
Year: 2023
Language: English
Pages: 469
Cover
Half Title
Title Page
Copyright Page
Contents
Acknowledgments
Foreword
Editors
Contributors
SECTION I: Historical Roots of GeoAI
Chapter 1: Introduction to Geospatial Artificial Intelligence (GeoAI)
Chapter 2: GeoAIās Thousand-Year History
Chapter 3: Philosophical Foundations of GeoAI
SECTION II: GeoAI Methods
Chapter 4: GeoAI Methodological Foundations: Deep Neural Networks and Knowledge Graphs
Chapter 5: GeoAI for Spatial Image Processing
Chapter 6: Spatial Representation Learning in GeoAI
Chapter 7: Intelligent Spatial Prediction and Interpolation Methods
Chapter 8: Heterogeneity-Aware Deep Learning in Space: Performance and Fairness
Chapter 9: Explainability in GeoAI
Chapter 10: Spatial Cross-Validation for GeoAI
SECTION III: GeoAI Applications
Chapter 11: GeoAI for the Digitization of Historical Maps
Chapter 12: Spatiotemporal AI for Transportation
Chapter 13: GeoAI for Humanitarian Assistance
Chapter 14: GeoAI for Disaster Response
Chapter 15: GeoAI for Public Health
Chapter 16: GeoAI for Agriculture
Chapter 17: GeoAI for Urban Sensing
SECTION IV: Perspectives for the Future of GeoAI
Chapter 18: Reproducibility and Replicability in GeoAI
Chapter 19: Privacy and Ethics in GeoAI
Chapter 20: A Humanistic Future of GeoAI
Chapter 21: Fast Forward from Data to Insight: (Geographic) Knowledge Graphs and Their Applications
Chapter 22: Forward Thinking on GeoAI
Index