1st Edition
Handbook of Geospatial Artificial Intelligence
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.
Section 1: Historical Roots of GeoAI
1. Introduction to Geospatial Artificial Intelligence (GeoAI)
Song Gao, Yingjie Hu, and Wenwen Li
2. GeoAI’s Thousands Years of History
Helen Couclelis
3. Philosophical Foundations of GeoAI
Krzysztof Janowicz
Section 2: GeoAI Methods
4. GeoAI Methodological Foundations: Deep Neural Networks and Knowledge Graphs
Song Gao, Jinmeng Rao, Yunlei Liang et al.
5. GeoAI for Spatial Image Processing
Samantha T. Arundel, Kevin G. McKeehan,Wenwen Li et al.
6. Spatial Representation Learning in GeoAI
Gengchen Mai, Ziyuan Li, and Ni Lao
7. Intelligent Spatial Prediction and Interpolation Methods
Di Zhu and Guofeng Cao
8. Heterogeneity-Aware Deep Learning in Space: Performance and Fairness
Yiqun Xie, Xiaowei Jia, Weiye Chen et al.
9. Explainability in GeoAI
Ximeng Cheng, Marc Vischer, Zachary Schellin et al.
10. Spatial Cross-Validation for GeoAI
Kai Sun, Yingjie Hu, Gaurish Lakhanpal et al.
Section 3: GeoAI Applications
11. GeoAI for the Digitization of Historical Maps
Yao-Yi Chiang, Muhao Chen, Weiwei Duan et al.
12. Spatiotemporal AI for Transportation
Tao Cheng, James Haworth, and Mustafa Can Ozkan
13. GeoAI for Humanitarian Assistance
Philipe A. Dias, Thomaz Kobayashi-Carvalhaes, Sarah Walters et al.
14. GeoAI for Disaster Response
Lei Zou, Ali Mostafavi, Bing Zhou et al.
15. GeoAI for Public Health
Andreas Zu¨fle, Taylor Anderson, Hamdi Kavak, et al.
16. GeoAI for Agriculture
Chishan Zhang, Chunyuan Diao, and Tianci Guo
17. GeoAI for Urban Sensing
Filip Biljecki
Section 4: Perspectives for the Future of GeoAI
18. Reproducibility and Replicability in GeoAI
Peter Kedron, Tyler D. Hoffman, and Sarah Bardin
19. Privacy and Ethics in GeoAI
Grant McKenzie, Hongyu Zhang, and S´ebastien Gambs
20. A Humanistic Future of GeoAI
Bo Zhao and Jiaxin Feng
21. (Geographic) Knowledge Graphs and Their Applications
Krzysztof Janowicz, Kitty Currier, Cogan Shimizu et al.
22. Forward Thinking on GeoAI
Shawn Newsam
Biography
Song Gao is an Assistant Professor and the Director of Geospatial Data Science Lab at the University of Wisconsin-Madison. He holds a Ph.D. degree in Geography from the University of California-Santa Barbara. His research interests are on Spatial Data Science and GeoAI approaches to Human Mobility and Social Sensing. He has authored and co-authored over 50 peer-reviewed articles in prominent journals and conference proceedings. He is the recipient of various research and teaching awards at the university, state, and international levels, including the Waldo Tobler Young Researcher Award in GIScience. He serves as the Associate Editor for Annals of GIS, and editorial board member for Scientific Reports, PLOS One, and Guest Editor for IJGIS, TGIS, and GeoInformatica. He has been a lead organizer for the AAG symposiums on GeoAI and Deep Learning and and for the ACM SIGSPATIAL GeoAI workshops.
Yingjie Hu is an Assistant Professor in the Department of Geography at the University at Buffalo, NY, and at the National Center for Geographic Information and Analysis (NCGIA). He holds a PhD from the Department of Geography at UC Santa Barbara. He is the author of over 50 peer-reviewed articles in top international journals and conferences. He and his work received awards at international, national, and university levels, including Waldo-Tobler Young Researcher Award, GIScience 2018 Best Full Paper Award, and others. His research was also covered by major media such as Reuters and VOA News.
Wenwen Li is a Full Professor in the School of Geographical Sciences and Urban Planning, Arizona State University, where she heads the CyberInfrastructure and Computation Intelligence Lab. Li's work has been applied to several scientific disciplines, including polar science, climatology, public health, hydrology and urban studies. Her research has been supported by various funding agencies, including the National Science Foundation (NSF), United States Geological Survey (USGS), and Open Geospatial Consortium. Li was the chair of the Association of American Geographers' cyber-infrastructure specialty group from 2013-2014; a member of the Spatial Decision Support Consortium at the University of the Redlands (2015-); and a graduate faculty member in the Computer Science program at ASU (2016-). Li is also the 2015 NSF CAREER award winner and 2021 NSF Mid-CAREER award winner.
