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GeoAI for Earth Observation Imagery

Fundamentals and Practical Applications

Paperback Engels 2026 9780443437960
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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.

Specificaties

ISBN13:9780443437960
Taal:Engels
Bindwijze:Paperback

Lezersrecensies

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Inhoudsopgave

<p>Part I – Image Preprocessing<br>Chapter 1. Earth observation and GeoAI through the years: six decades of progress in image analysis<br>Chapter 2. Radiometric correction<br>Chapter 3. Rectification<br>Chapter 4. Georeferencing of remote sensing imagery<br>Chapter 5. Image registration<br>Chapter 6. Mosaicking remote sensing data: techniques, challenges, and innovations [LUNGAD_FM_Q | PDF]</p><p>Part II – Image Enhancement<br>Chapter 7. Pansharpening<br>Chapter 8. Superresolution of satellite imagery<br>Chapter 9. Earth observation image denoising [LUNGAD_FM_Q | PDF]</p><p>Part III – Image Analysis<br>Chapter 10. Semantic segmentation of Earth observation data<br>Chapter 11. Synthesis of Earth observation imagery<br>Chapter 12. Geospatial data visualization with Python<br>Chapter 13. Multimodal data fusion for semantic mapping and change detection<br>Chapter 14. Self-supervised learning for Earth observation foundation models<br>Chapter 15. Object detection in remote sensing<br>Chapter 16. A tour of visual question answering for remote sensing [LUNGAD_FM_Q | PDF]</p><p>Part IV – Computing<br>Chapter 17. Geospatial machine learning libraries<br>Chapter 18. High-performance computing for geospatial intelligence<br>Chapter 19. Cloud infrastructure for Earth observation imagery</p>

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€ 196,59
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        GeoAI for Earth Observation Imagery