Geospatial Artificial Intelligence (GeoAI) integrates AI with spatial data to revolutionize fields like urban planning, environmental monitoring, and disaster management. The books below are selected for their direct relevance to GeoAI, focusing on machine learning, deep learning, spatial analytics, and real-world applications. Authored by leading experts, these works provide essential knowledge for students, researchers, and professionals advancing GeoAI.

1. Handbook of Geospatial Artificial Intelligence by Song Gao, Yingjie Hu, and Wenwen Li 

Published in 2023, this comprehensive handbook, edited by Song Gao, Yingjie Hu, and Wenwen Li, is a definitive GeoAI resource, covering over 30 topics from machine learning to spatial visualization. It systematically explores GeoAI’s theoretical foundations and practical applications, integrating AI, deep learning, and knowledge graph technologies.

The book includes code examples, datasets, and case studies on environmental observation, social sensing, and disaster response, making it ideal for hands-on learning. Its contributions from top experts ensure it’s a must-have for mastering GeoAI across diverse applications.

Key Highlights

• Over 30 chapters on GeoAI theory and applications
• Includes code examples and datasets
• Covers environmental and disaster response applications
• Authored by leading GeoAI scholars

 Book Details

• Available at: crcpress.com/Handbook-of-Geospatial-Artificial-Intelligence/Gao-Hu-Li/p/book/9781032311661
• Publisher: CRC Press
• Year: 2023
• Format: Hardcover, eBook
• ISBN: 978-1-032-31166-1
• Pages: 448

Who It’s Best For

• GeoAI researchers and educators
• Professionals seeking a comprehensive GeoAI reference
• Students studying geospatial data science

2. Geospatial Data Analytics on AWS by Scott Bateman, Janahan Gnanachandran, and Jeff DeMuth 

This 2023 book by Scott Bateman, Janahan Gnanachandran, and Jeff DeMuth explores managing and analyzing geospatial data using cloud-based AI tools on AWS. It covers machine learning and deep learning for processing large-scale spatial datasets, offering practical solutions for GeoAI practitioners.

The book provides tutorials on AWS services like SageMaker and GeoSpatial ML, with case studies on urban analytics and environmental monitoring. Its focus on cloud computing makes it essential for professionals scaling GeoAI applications in real-world settings.

Key Highlights

• Focuses on GeoAI with AWS cloud tools
• Includes tutorials and case studies
• Covers urban and environmental applications
• Practical for cloud-based GeoAI

Book Details

• Available at: packtpub.com/product/geospatial-data-analytics-on-aws/9781804613825
• Publisher: Packt Publishing
• Year: 2023
• Format: Paperback, eBook
• ISBN: 978-1-80461-382-5
• Pages: Approx. 320

Who It’s Best For

• Data scientists using cloud platforms
• Professionals in urban analytics
• GeoAI practitioners scaling applications

3. GeoAI and Human Geography: The Dawn of a New Spatial Intelligence Era by Xiao Huang 

Published in 2025, this book by Xiao Huang examines GeoAI’s transformative role in human geography, focusing on disaster management, climate adaptation, and urban planning. It explores how AI enhances spatial intelligence, offering new perspectives on human-environment interactions, making it a strong alternative for human-centered GeoAI studies.

The book includes case studies on flood prediction and urban mobility, combining theoretical insights with practical GeoAI tools. Its interdisciplinary approach makes it ideal for researchers and students bridging geography and AI, with a focus on societal applications.

Key Highlights

• Focuses on GeoAI in human geography
• Includes disaster and urban case studies
• Explores spatial intelligence trends
• Published by a leading academic press

Book Details

• Available at: link.springer.com/book/9783031874208
• Publisher: Springer
• Year: 2025
• Format: Hardcover, eBook
• ISBN: 978-3-031-87420-8
• Pages: Approx. 300

Who It’s Best For

• Geographers and urban planners
• Researchers in disaster management
• Students exploring GeoAI applications

4. Advances in Scalable and Intelligent Geospatial Analytics by Surya S. Durbha, Jibonananda Sanyal, and Others 

This 2023 edited collection, led by Surya S. Durbha and Jibonananda Sanyal, focuses on scalable GeoAI techniques for large-scale geospatial analytics. It covers machine learning and deep learning for applications like remote sensing and urban modeling, addressing data volume and complexity challenges.

The book includes research papers on satellite imagery analysis and real-time disaster response, offering practical insights for professionals. Its emphasis on scalability makes it a key resource for GeoAI practitioners handling big data.

Key Highlights

• Covers scalable GeoAI techniques
• Includes remote sensing and disaster applications
• Research-oriented with practical insights
• Published by a reputable publisher

Book Details

• Available at: crcpress.com/Advances-in-Scalable-and-Intelligent-Geospatial-Analytics/Durbha-Sanyal/p/book/9781032200316
• Publisher: CRC Press
• Year: 2023
• Format: Hardcover, eBook
• ISBN: 978-1-032-20031-6
• Pages: Approx. 400

Who It’s Best For

• Data scientists handling large datasets
• Professionals in remote sensing
• Researchers in scalable GeoAI

5. GeoAI and its Role in Planetary Health by Abhijeet Sarkar 

Published in 2024, this book by Abhijeet Sarkar examines GeoAI’s role in addressing planetary health challenges, such as climate change and biodiversity loss. It explores AI-driven spatial analytics to support global sustainability, making it highly relevant for environmental GeoAI.

The book includes case studies on deforestation monitoring and urban heat mapping, offering practical tools for researchers and policymakers. Its focus on sustainability aligns with emerging GeoAI trends in environmental science.

Key Highlights

• Focuses on GeoAI for planetary health
• Includes environmental case studies
• Supports sustainability goals
• Relevant for global challenges

Book Details

• Available at: amazon.com/GeoAI-Role-Planetary-Health-Sustainability/dp/B0DT6ZPH75
• Publisher: Independently Published
• Year: 2024
• Format: Paperback, eBook
• ISBN: 979-8-89699-263-9
• Pages: Approx. 200

Who It’s Best For

• Environmental researchers
• Policymakers in sustainability
• GeoAI practitioners in ecology

6. Deep Learning for the Earth Sciences by Gustau Camps-Valls, Devis Tuia, and Xiao Xiang Zhu

Published in 2021, this book by Gustau Camps-Valls, Devis Tuia, and Xiao Xiang Zhu provides a practical guide to deep learning in remote sensing, climate science, and geosciences. It bridges AI theory with applications like satellite imagery analysis, making it highly relevant for GeoAI.

The book includes code examples and case studies on environmental monitoring and geological mapping, ensuring value for Earth scientists and GeoAI practitioners seeking practical tools.

Key Highlights

• Applies deep learning to satellite data
• Includes code examples and case studies
• Interdisciplinary GeoAI applications
• Widely cited in Earth sciences

Book Details

• Available at: amazon.com/Deep-Learning-Earth-Sciences-Comprehensive/dp/1119646146
• Publisher: Wiley
• Year: 2021
• Format: Hardcover, eBook
• ISBN: 978-1-119-64614-3
• Pages: 432

Who It’s Best For

• Earth science researchers
• Data scientists in remote sensing
• Students in GeoAI applications

7. Applied Geospatial Data Science with Python by David S. Jordan

This 2023 book by David S. Jordan provides a practical guide to GeoAI and geospatial data science using Python, focusing on environmental applications. It covers machine learning and spatial analysis techniques, making it ideal for hands-on learners in GeoAI.

The book includes code examples and case studies on pollution mapping and habitat modeling, ensuring relevance for environmental scientists and GeoAI practitioners seeking practical tools.

Key Highlights

• Practical GeoAI with Python
• Includes environmental case studies
• Hands-on code examples
• Relevant for data scientists

Book Details

• Available at: packtpub.com/product/applied-geospatial-data-science-with-python/9781803238128
• Publisher: Packt Publishing
• Year: 2023
• Format: Paperback, eBook
• ISBN: 978-1-80323-812-8
• Pages: Approx. 280

Who It’s Best For

• Environmental data scientists
• Python users in GeoAI
• Students learning geospatial analytics

8. Learning Geospatial Analysis with Python by Joel Lawhead

Joel Lawhead’s 2023 (4th edition) book is a comprehensive guide to geospatial analysis and GeoAI using Python, focusing on GIS and remote sensing. It covers machine learning and deep learning techniques, ensuring relevance for modern GeoAI applications.

The book includes tutorials and code examples for spatial modeling and image processing, making it practical for students and professionals. Its updated content reflects the latest GeoAI trends and tools.

Key Highlights

• Comprehensive GeoAI with Python
• Includes GIS and remote sensing tutorials
• Updated 4th edition
• Practical for hands-on learning

Book Details

• Available at: packtpub.com/product/learning-geospatial-analysis-with-python-fourth-edition/9781837639175
• Publisher: Packt Publishing
• Year: 2023
• Edition: 4th
• Format: Paperback, eBook
• ISBN: 978-1-83763-917-5
• Pages: Approx. 450

Who It’s Best For

• GIS students and professionals
• Data scientists in remote sensing
• Python users in GeoAI

9. Python for Geospatial Data Analysis by Bonny P. McClain

Published in 2022, this book by Bonny P. McClain focuses on GeoAI and geospatial data analysis using Python, with applications in location intelligence and urban analytics. It provides practical tools for spatial data processing and machine learning, making it accessible for GeoAI practitioners.

The book includes case studies on urban planning and environmental analysis, ensuring value for professionals and students. Its straightforward approach makes it ideal for those new to GeoAI or seeking practical skills.

Key Highlights

• Focuses on GeoAI with Python
• Includes urban and environmental case studies
• Accessible for beginners
• Practical for location intelligence

Book Details

• Available at: amazon.com/Python-Geospatial-Data-Analysis-Intelligence/dp/109810479X
• Publisher: O’Reilly Media
• Year: 2022
• Format: Paperback, eBook
• ISBN: 978-1-09810-479-5
• Pages: Approx. 300

Who It’s Best For

• Urban analysts and data scientists
• Students learning GeoAI
• Professionals in location intelligence

10. Advances in Machine Learning and Image Analysis for GeoAI by Saurabh Prasad, Jocelyn Chanussot, and Jun Li

Published in 2024, this book by Saurabh Prasad, Jocelyn Chanussot, and Jun Li explores cutting-edge machine learning techniques for GeoAI, with a focus on image analysis in geospatial contexts. It delves into advanced algorithms for processing satellite imagery and spatial data, addressing applications like land cover classification and disaster response.

The book includes practical case studies and code examples, making it a valuable resource for GeoAI practitioners working with large-scale image datasets. Its emphasis on scalable machine learning ensures relevance for researchers and professionals tackling complex geospatial challenges.

Key Highlights

• Focuses on machine learning for GeoAI image analysis
• Includes case studies on satellite imagery
• Offers practical code examples
• Relevant for disaster response applications 

Book Details

• Available at: shop.elsevier.com/books/advances-in-machine-learning-and-image-analysis-for-geoai/prasad/978-0-443-19077-3
• Publisher: Elsevier
• Year: 2024
• Format: Hardcover, eBook
• ISBN: 978-0-323-85277-7
• Pages: Approx. 360

Who It’s Best For

• Data scientists in image analysis
• GeoAI researchers in remote sensing
• Professionals in disaster response

11. GeoAI for Earth Observation Imagery by Dalton Lunga and Ronny Hänsch

This 2024 book by Dalton Lunga and Ronny Hänsch provides a comprehensive guide to GeoAI applications in earth observation imagery, focusing on deep learning for satellite data analysis. It covers techniques for object detection, change detection, and environmental monitoring, making it highly relevant for GeoAI-driven earth sciences.

The book includes tutorials and case studies on disaster management and climate modeling, offering practical tools for researchers and practitioners. Its focus on real-world applications ensures it’s a key resource for advancing GeoAI in earth observation.

Key Highlights

• Covers deep learning for earth observation
• Includes disaster and climate case studies
• Offers practical tutorials
• Focused on satellite data analysis 

Book Details

• Available at: amazon.com/GeoAI-Earth-Observation-Imagery-Fundamentals-ebook/dp/B0DXDYSKB8
• Publisher: Springer
• Year: 2024
• Format: eBook
• Pages: Approx. 320

Who It’s Best For

• Earth scientists using satellite data
• GeoAI practitioners in disaster management
• Researchers in environmental monitoring

12. Geoinformatics in Sustainable Ecosystem and Society by Yichun Xie, Yong Li, and Others

Published in 2020, this edited collection by Yichun Xie, Yong Li, and others explores GeoAI applications in sustainable ecosystems and societal development. It covers machine learning and spatial analytics for environmental management, urban planning, and resource conservation, offering a multidisciplinary perspective.

The book includes research papers on topics like biodiversity mapping and sustainable urban growth, making it valuable for researchers and policymakers. Its focus on sustainability aligns with GeoAI’s growing role in addressing global challenges.

Key Highlights

• Focuses on GeoAI for sustainability
• Includes biodiversity and urban case studies
• Multidisciplinary research papers
• Relevant for environmental management 

Book Details

• Available at: amazon.com/Geoinformatics-Sustainable-Ecosystem-Society-Communications/dp/9811561052
• Publisher: Springer
• Year: 2020
• Format: Paperback, eBook
• ISBN: 978-981-15-6105-4
• Pages: Approx. 460

Who It’s Best For

• Environmental researchers
• Urban planners using GeoAI
• Policymakers in sustainability

13. Esri Advanced Guide to Python in ArcGIS by Dave Crawford and Daniel Yaw

This 2023 book by Dave Crawford and Daniel Yaw provides an advanced guide to using Python for GeoAI within the ArcGIS platform. It focuses on machine learning and spatial analytics for geospatial data processing, offering practical techniques for automating GIS workflows and analyzing spatial patterns.

The book includes tutorials and code examples tailored to ArcGIS users, covering applications like urban analytics and environmental mapping. Its hands-on approach makes it ideal for GIS professionals and data scientists integrating GeoAI into ArcGIS environments.

Key Highlights

• Focuses on GeoAI with ArcGIS and Python
• Includes tutorials for GIS automation
• Covers urban and environmental applications
• Practical for ArcGIS users

Book Details

• Available at: amazon.com/Advanced-Guide-Python-ArcGIS/dp/1589488237
• Publisher: Esri Press
• Year: 2023
• Format: Paperback, eBook
• ISBN: 978-1-58948-823-6
• Pages: Approx. 400

Who It’s Best For

• GIS professionals using ArcGIS
• Data scientists in spatial analytics
• GeoAI practitioners automating workflows

From FlyPix AI: Enhancing GeoAI with Cutting-Edge Geospatial Automation

At FlyPix AI, we are proud to contribute to the advancement of GeoAI by offering an innovative AI-powered geospatial and imagery analytics platform that seamlessly integrates with NetSuite ERP systems. Our solution transforms geospatial intelligence by automating real-time tasks such as asset tracking, site monitoring, and logistics optimization using data from drones, satellites, and LiDAR. Industries like logistics and inventory management benefit from automated processes, including stock distribution analysis and delivery route planning, embedded directly into NetSuite workflows.

Our no-code platform empowers users to create custom workflows and generate visualizations like heatmaps and 3D overlays without requiring technical expertise. Fully compatible with NetSuite’s SuiteCloud and GIS systems, FlyPix AI ensures secure, scalable automation, delivering high-precision insights for faster decision-making and operational efficiency.

Connection to GeoAI Literature

FlyPix AI’s capabilities align with the GeoAI advancements highlighted in the article Top GeoAI Books: Pioneering Geospatial Intelligence. Books like Handbook of Geospatial Artificial Intelligence and GeoAI for Earth Observation Imagery  emphasize AI-driven spatial analytics and real-time applications, which resonate with our platform’s focus on automating geospatial workflows. Similarly, Geospatial Data Analytics on AWS  explores cloud-based AI tools, complementing FlyPix AI’s scalable, cloud-integrated solutions. Our platform brings these concepts to life, offering practical tools for researchers, professionals, and organizations to implement GeoAI in real-world scenarios, from urban analytics to disaster response.

FlyPix AI is committed to pushing the boundaries of geospatial intelligence, empowering users to harness GeoAI for smarter, faster, and more sustainable decision-making. Explore our platform at flypix.ai and join us in shaping the future of GeoAI.

Conclusion

These GeoAI books provide a wealth of knowledge for advancing geospatial intelligence. From comprehensive handbooks to practical Python guides, they cover deep learning, urban analytics, disaster response, and more, catering to students, researchers, and professionals. These resources are essential for staying at the forefront of GeoAI innovation.

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