Dr. Sartajvir Singh is an accomplished academician at Chandigarh University, specializing in Electronics and Communication Engineering. He is practicing as Registered Patent Agent (IN/PA 5806). So far, he has filed 58 innovations/patents at the Indian Patent Office (IPO) with 35 Granted/Awarded. With expertise in the filed of Remote Sensing, Computing Algorithms, and Electronics, he has published 72 in SCI/Scopus Index Journals. An active IEEE member, he engages with IEEE Young Professionals, IEEE GRSS, IEEE Sensors Council, and ISPRS. His research focuses on Remote Sensing, Machine Learning, Deep Learning, and Computer Vision. He has served as a keynote speaker and resource person for numerous workshops and webinars. He also edited 2 conference proceedings, 7 books with publishers like Wiley, De Gruyter, Bentham, Elsevier, CRC, and Apple Academic Press.
Google Earth Engine and Artificial Intelligence for Earth Observation:Algorithms to Sustainable Applications
Publisher: Elsevier - Academic Press
Editors: Vishakha Sood, Dileep Kumar Gupta, Sartajvir Singh, Biswajeet Pradhan
This book provides effective guidance to explore the potential of the Google Earth Engine (GEE) in earth observation using state-of-art Artificial Intelligence (AI) driven tools and technologies such as deep learning, machine learning, cloud computing, big data analytics, and many more. With these tools and technologies associated with GEE, the applicability will be enhanced in the various scientific domains of remote sensing and geographic information system (GIS) such as Water resource management, agriculture mapping, forest cover, climate change, natural hazard assessment, aquatic and hydrological applications and many more. Additionally, it also highlights the challenges and futuristic AI-driven tools and technologies for earth observation data analytics.
| Important Dates | |
|---|---|
| Abstract Submission Deadline | 31 October 2023 |
| Abstract Acceptance Notification | 15 November 2023 |
| Full Chapter Submission Deadline | 15 December 2023 |
| Chapter Acceptance Notification | 31 January 2023 |
| Projected Book Release Date | Jan 2025 |
| Important Guidelines | |
|---|---|
| Citation Style | IEEE |
| Originality | Plagiarism Under 10%, 0% AI Generated Content |
| Text Style | 11 pt Times New Roman, 1.5 line spacing |
| Headings | 3 numbered headings (e.g. 1, 1.1, 1.1.1), one unnumbered heading |
| Figures | High-quality, original figures, 300 dpi |
Chapter 1: Cloud computing platforms based remote sensing big data applications.
Chapter 2: Role of GEE in earth observation via remote sensing
Chapter 3: Applications of GEE in sustainable society and environment
Chapter 4: Sustainable Remote Sensing Data Analysis using GEE and AI
Chapter 5: Systematic survey on GEE-based projects and their perspectives
Chapter 6: A comprehensive review of emerging AI-based Machine and DL algorithms for GEE
Chapter 7: Comparative Analysis of various Machine and Deep learning classification algorithms
Chapter 8: Estimation of land-use land-cover variations using GEE and AI-based change detection tools
Chapter 9: Monitoring and mapping of urban development with integration of GEE and AI
Chapter 10: Image fusion of optical and microwave satellite datasets using deep neural networks
Chapter 11: AI-driven cloud-based remote sensing for big data analysis
Chapter 12: Remote sensing for Water resource management with GEE
Chapter 13: Agriculture mapping for crop monitoring using remote sensing and GEE
Chapter 14: Mapping and monitoring of forest resources and activities using GEE
Chapter 15: Response to climate change using AI and cloud computing platforms
Chapter 16: Role of GEE in natural hazard monitoring and management
Chapter 17: Estimation of Snow/ice cover parameters using GEE and AI
Chapter 18: Challenges and limitations of the cloud-based platforms
Chapter 19: Futuristic AI-driven tools and technologies for earth observation data analytics
Chapter 20: Exploration of the science of remote sensing and GIS with GEE
Chapter 21: Creative integration of GEE with AI for algorithms to applications
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