GeoAI for Earth Observation Imagery

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

Buch, Englisch, 498 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

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

Inhaltsverzeichnis

Part I – Image Preprocessing
Chapter 1. Earth observation and GeoAI through the years: six decades of progress in image analysis
Chapter 2. Radiometric correction
Chapter 3. Rectification
Chapter 4. Georeferencing of remote sensing imagery
Chapter 5. Image registration
Chapter 6. Mosaicking remote sensing data: techniques, challenges, and innovations [LUNGAD_FM_Q PDF]

Part II – Image Enhancement
Chapter 7. Pansharpening
Chapter 8. Superresolution of satellite imagery
Chapter 9. Earth observation image denoising [LUNGAD_FM_Q PDF]

Part III – Image Analysis
Chapter 10. Semantic segmentation of Earth observation data
Chapter 11. Synthesis of Earth observation imagery
Chapter 12. Geospatial data visualization with Python
Chapter 13. Multimodal data fusion for semantic mapping and change detection
Chapter 14. Self-supervised learning for Earth observation foundation models
Chapter 15. Object detection in remote sensing
Chapter 16. A tour of visual question answering for remote sensing [LUNGAD_FM_Q PDF]

Part IV – Computing
Chapter 17. Geospatial machine learning libraries
Chapter 18. High-performance computing for geospatial intelligence
Chapter 19. Cloud infrastructure for Earth observation imagery

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.