Handbook of Geospatial Artificial Intelligence / edited by Song Gao, Yingjie Hu, and Wenwen Li.
2024
Details
Title
Handbook of Geospatial Artificial Intelligence / edited by Song Gao, Yingjie Hu, and Wenwen Li.
Language
English
Edition
First edition.
Imprint
Boca Raton : CRC Press, 2024.
Description
xx, 448 p. ;
Digital File Characteristics
text file PDF
text file online
text file online
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Formatted Contents Note
Introduction
Chapter 1: Introduction to Geospatial Artificial Intelligence (GeoAI)
Chapter 2: GeoAIās Thousand-Year History
Chapter 3: Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science
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 inSpace: Performance and Fairness
Chapter 9: Explainability in GeoAI
Chapter 10: Spatial Cross-Validation for GeoAI
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 forUrbanSensing
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
Chapter 1: Introduction to Geospatial Artificial Intelligence (GeoAI)
Chapter 2: GeoAIās Thousand-Year History
Chapter 3: Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science
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 inSpace: Performance and Fairness
Chapter 9: Explainability in GeoAI
Chapter 10: Spatial Cross-Validation for GeoAI
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 forUrbanSensing
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
Access Note
Access to PDF and online versions restricted to ITU users
Linked Resources
Full text - Online access via publisher's web site
ISBN
9781003308423 (ebook)
Record Appears in
General Collection