GeoAI for Earth Observation Imagery

Fundamentals and Practical Applications
Erscheinungsjahr 2026
ISBN: 978-0-443-43796-0
Verlag: Elsevier - Health Sciences Division

Buch, Englisch, 400 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

GeoAI for Earth Observation Imagery: Fundamentals and Practical Applications comprehensively covers methodologies of AI and Machine Learning applications of image processing for Earth Observation (EO) Imagery. As traditional image processing methods face challenges with handling vast volumes of EO imagery, leading to efficiencies and limitations when extracting meaningful insights, AI-driven approaches can enhance the efficiency, accuracy, and scalability of image processing. Chapters cover essential methodologies including atmospheric compensation, image enhancement techniques like deblurring and superresolution, and advanced analysis methods such as semantic segmentation and object detection.

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.

Autoren/Hrsg.

Lunga, Dalton

Hänsch, Ronny

Fachgebiete

• Geowissenschaften Geologie Geologie
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Part I - Image Preprocessing
1. Atmospheric Compensation
2. Rectification
3. Geocoding
4. Image Registration
5.Mosaicking

Part II - Image Enhancement
6. Image Restoration/Deblurring
7. Pansharpening
8. Superresolution
9. Denoising

Part III - Image Analysis
10. Semantic Segmentation
11. Synthesis
12. Visualization
13. Data Fusion
14. Foundation Models/Self-Supervised Learning/Fine-tuning
15. Object Detection
16. Visual Question Answering (VQA)

Part IV - Computing
17. Geospatial Libraries
18. Machine Learning Libraries
19. High Performance Computing
20. Cloud Computing
21. Conclusions/Future Perspectives

Über Autor(innen)

Hänsch, Ronny
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).

Lunga, Dalton
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.