GeoAI for Earth Observation Imagery : Fundamentals and Practical Applications

Lunga, Dalton (EDT)/ Hänsch, Ronny (EDT)

Elsevier - Health Sciences Division（2026/05発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 498 p.
• 言語 ENG
• 商品コード 9780443437960
• DDC分類 910.285

Full Description

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.

Contents

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