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
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
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
3. Rectification
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
4. Georeferencing of remote sensing imagery
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
5. Image registration
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
6. Mosaicking remote sensing data
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
Part II — Image enhancement
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
8. Superresolution of satellite imagery
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
9. Earth observation image denoising
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
Part III — Image analysis
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
11. Synthesis of Earth observation imagery
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
12. Geospatial data visualization with Python
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
13. Multimodal data fusion
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
14. Self-supervised learning
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
15. Object detection
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
16. Visual question answering
16.1. Introduction
16.2. Datasets
16.3. Methods
16.4. Conclusion
Part IV — Computing
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
18. High-performance computing
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
19. Cloud infrastructure for EO imagery
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 19, 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).