GeoAI for Earth Observation Imagery
Fundamentals and Practical Applications
- 1st Edition - May 19, 2026
- Latest edition
- Editors: Dalton Lunga, Ronny Hänsch
- Language: English
GeoAI for Earth Observation Imagery: Fundamentals and Practical Applications comprehensively covers methodologies of AI and Machine Learning applications of image processing for Ea… Read more
Description
Description
Cutting-edge approaches to computing, automating, and optimizing image processing tasks are also covered. Additionally, emerging trends in GeoAi and their implication on future research are reviewed. The book serves as an essential guide for navigating the complexities of spatial data and equips readers with knowledge to enhance their analytical capabilities.
Key features
Key features
- Examines the essentials of image preprocessing, enhancement, analysis, and computing techniques tailored for EO imagery
- Provides an introductory resource for implementing AI for EO image analysis
- Demonstrates practical deployment of GeoML methodologies through case studies
Readership
Readership
Table of contents
Table of contents
Chapter 1
Earth observation and GeoAI through the years: six decades of progress in image analysis
- 1.1 Motivation and context
- 1.2 About this book
- References
Part I — Image preprocessing
Chapter 2: Radiometric correction- 2.1 Introduction
- 2.2 Methodology
- 2.3 State-of-the-art and current developments
- 2.4 Application example
- 2.5 Best practices and open issues
- 2.6 Future implications
- References
- 3.1 Introduction
- 3.2 Methodology
- 3.3 State of the art and current developments
- 3.4 Application example
- 3.5 Best practices and open issues
- 3.6 Future implications
- References
- 4.1 Introduction
- 4.2 Methodology
- 4.3 State of the art and current developments
- 4.4 Application example
- 4.5 Best practices and open issues
- 4.6 Future implications
- References
- 5.1 Introduction
- 5.2 Methodology
- 5.3 State-of-the-art and current developments
- 5.4 Application example
- 5.5 Best practices and open issues
- 5.6 Future implications
- References
- 6.1 Introduction
- 6.2 Methodology
- 6.3 State of the art and current developments
- 6.4 Future challenges and opportunities
- 6.5 A practitioner’s guide to mosaicking
- References
Part II — Image enhancement
Chapter 7: Pansharpening- 7.1 Introduction
- 7.2 Methodology
- 7.3 State-of-the-art and current developments
- 7.4 Application example
- 7.5 Best practices and open issues
- 7.6 Future implications
- References
- 8.1 Introduction
- 8.2 Methodology
- 8.3 State-of-the-art and current developments
- 8.4 Application example
- 8.5 Best practices and open issues
- 8.6 Future implications
- References
- 9.1 Introduction
- 9.2 Methodology
- 9.3 State-of-the-art and current developments
- 9.4 Application example
- 9.5 Best practices and open issues
- 9.6 Future implications
- References
Part III — Image analysis
Chapter 10: Semantic segmentation- 10.1 Introduction
- 10.2 Methodology
- 10.3 State-of-the-art and current developments
- 10.4 Application example
- 10.5 Best practices, challenges, future implications
- 10.6 SpaceNet 8 — copyright and licensing
- References
- 11.1 Introduction
- 11.2 Requirements for remote sensing
- 11.3 Methodology
- 11.4 State-of-the-art and current developments
- 11.5 Application example
- 11.6 Best practices and open issues
- 11.7 Future implications
- References
- 12.1 Introduction
- 12.2 Methodology
- 12.3 State-of-the-art and current developments
- 12.4 Application examples
- 12.5 Best practices and open issues
- 12.6 Future implications
- References
- 13.1 Introduction
- 13.2 Methodology
- 13.3 State-of-the-art and current developments
- 13.4 Application examples
- 13.5 Best practices and open issues
- 13.6 Future implications
- References
- 14.1 Introduction
- 14.2 Methodology
- 14.3 State-of-the-art and current developments
- 14.4 Example
- 14.5 Best practices and open issues
- 14.6 Future implications
- References
- 15.1 Introduction
- 15.2 Methodology
- 15.3 State-of-the-art and current developments
- 15.4 Application example
- 15.5 Best practices and open issues
- 15.6 Future implications
- References
- 16.1 Introduction
- 16.2 Datasets
- 16.3 Methods
- 16.4 Conclusion
- References
Part IV — Computing
Chapter 17: Geospatial machine learning libraries- 17.1 Introduction
- 17.2 Methodology
- 17.3 State-of-the-art and current developments
- 17.4 Application example
- 17.5 Best practices and open issues
- 17.6 Future implications
- References
- 18.1 Introduction
- 18.2 Modern HPC
- 18.3 Distributed computing
- 18.4 State-of-the-art and current developments
- 18.5 GeoAI applications
- 18.6 Best practices and open issues
- 18.7 Future implications
- References
- 19.1 Introduction
- 19.2 Methodology
- 19.3 State-of-the-art and current developments
- 19.4 Application examples
- 19.5 Best practices
- 19.6 Future implications
- 19.7 Conclusion
- References
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 27, 2026
- Language: English
About the editors
About the editors
DL
Dalton Lunga
Dalton Lunga is a group leader for GeoAI and a senior R&D staff scientist at ORNL. He is also an Associate Editor for Geoscience and Remote Sensing Letters. He is an interdisciplinary scientist with expertise in artificial intelligence, computer vision, high-performance computing and remote sensing. Dalton leads multidisciplinary teams and projects focused on developing novel methods at the intersection of AI, computer vision, and geography toward the built and physical environment mapping using earth observation data. His research is impacting the generation of accurate population estimates and information about urban growth and decline, informing disaster response, identifying at-risk areas to support national security application challenges. Prior to ORNL, Dalton was a Team Lead and Senior Research Scientist at the Council for Scientific and Industrial Research, South Africa where he established and led a Data Science for Decision Impact team. He received his Ph.D in Electrical and Computer Engineering from Purdue University, West Lafayette.
RH
Ronny Hänsch
Ronny Hänsch is a scientist at the Microwave and Radar Institute of the German Aerospace Center (DLR) where he leads the Machine Learning Team in the Signal Processing Group of the SAR Technology Department. His research interest is computer vision and machine learning with a focus on remote sensing (in particular SAR processing and analysis). He was chair of the GRSS Image Analysis and Data Fusion (IADF) technical committee 2021-23, and serves as co-chair of the ISPRS working group on Image Orientation and Sensor Fusion, as editor in chief of the Geoscience and Remote Sensing Letters. associate editor the ISPRS Journal of Photogrammetry and Remote Sensing, and organizer of the CVPR Workshop EarthVision (2017-2024) and the IGARSS Tutorial on Machine Learning in Remote Sensing (2017-2024). He has extensive experience in organizing remote sensing community competitions (e.g. SpaceNet and the GRSS Data Fusion Contest).