HANDBOOK OF GEOSPATIALARTIFICIAL INTELLIGENCE

Song Gao, Yingjie Hu, Wenwen Li

Abril de 2026       Páginas: 468       Edición en tapa blanda

Código 12046        ISBN/EAN: 9781032311678

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.

TABLE OF CONTENTS:

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