GeoAI for Earth Observation Imagery

Fundamentals and Practical Applications

Häftad, Engelska, 2026

2 189 kr

Kommande

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.

Examines the essentials of image preprocessing, enhancement, analysis, and computing techniques tailored for Earth Observation imagery
Demonstrates real-world applications of AI and Machine Learning methodologies through case studies
Provides an introductory resource for implementing AI in Earth Observation imagery

Produktinformation

Utgivningsdatum2026-07-01
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor498
FörlagElsevier Science
ISBN9780443437960

Tillhör följande kategorier

Företagsekonomi inom Ekonomi och Ledarskap
Geovetenskap inom Naturvetenskap och teknik
Affärsapplikationer inom Data och IT
Geografi inom Naturvetenskap och teknik

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.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).

Part I - Image Preprocessing1. Atmospheric Compensation2. Rectification3. Geocoding4. Image Registration5.MosaickingPart II - Image Enhancement6. Image Restoration/Deblurring7. Pansharpening8. Superresolution9. DenoisingPart III - Image Analysis10. Semantic Segmentation11. Synthesis12. Visualization13. Data Fusion14. Foundation Models/Self-Supervised Learning/Fine-tuning15. Object Detection16. Visual Question Answering (VQA)Part IV - Computing17. Geospatial Libraries18. Machine Learning Libraries19. High Performance Computing20. Cloud Computing21. Conclusions/Future Perspectives