Product details:
| ISBN13: | 9780443273728 |
| ISBN10: | 0443273723 |
| Binding: | Paperback |
| No. of pages: | 508 pages |
| Size: | 229x152 mm |
| Language: | English |
| 700 |
Category:
Google Earth Engine and Artificial Intelligence for Earth Observation
Algorithms and Sustainable Applications
Series:
Earth Observation;
Publisher: Elsevier
Date of Publication: 1 March 2025
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Long description:
Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications explores a wide range of transformative data fusion techniques of Artificial Intelligence (AI) technologies applied to Google Earth Engine (GEE) techniques. The book includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. It delves into the challenges of AI-driven tools and technologies for Earth observation data analysis, offering possible solutions and directly addressing current and upcoming needs within Earth Observation.
This is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.
This is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.
- Includes utilization of AI with GEE tools for a spectrum od scientific domains in remote sensing and geographic information systems (GIS), such as natural hazard assessment, aquatic and hydrological applications, and forest cover
- Highlights the challenges and possible solutions for AI-driven tools and technologies for Earth observation data analysis
- Provides detailed case studies that show specific considerations and exceptions for applications of AI in GEE for Earth observation
Table of Contents:
Section A: GEE cloud computing based Remote Sensing
1. Cloud computing platforms based remote sensing big data applications
2. Role of GEE in earth observation via remote sensing
3. Applications of GEE in sustainable society and environment
4. Sustainable Remote Sensing Data Analysis using GEE and AI
5. Systematic survey on GEE-based projects and their perspectives
Section B: AI-based GEE tool and technologies
6. A comprehensive review of emerging AI-based Machine and Deep learning algorithms for GEE
7. Comparative Analysis of various Machine and Deep learning classification algorithms
8. Estimation of land-use land-cover variations using GEE and AI-based change detection tools
9. Monitoring and mapping of urban development with integration of GEE and AI
10. Image fusion of optical and microwave satellite datasets using deep neural networks
11. AI-driven cloud-based remote sensing for big data analysis
Section C: Emerging applications and case studies of GEE in earth observation
12. Remote sensing for Water resource management with GEE
13. Agriculture mapping for crop monitoring using remote sensing and GEE
14. Mapping and monitoring of forest resources and activities using GEE
15. Response to climate change using AI and cloud computing platforms
16. Role of GEE in natural hazard monitoring and management
17. Estimation of Snow/ice cover parameters using GEE and AI
Section D: Challenges and future trends of GEE
18. Challenges and limitations of the cloud-based platforms
19. Futuristic AI-driven tools and technologies for earth observation data analytics
20. Exploration of the science of remote sensing and GIS with Google Earth Engine
21. Creative integration of GEE with AI for algorithms to applications
1. Cloud computing platforms based remote sensing big data applications
2. Role of GEE in earth observation via remote sensing
3. Applications of GEE in sustainable society and environment
4. Sustainable Remote Sensing Data Analysis using GEE and AI
5. Systematic survey on GEE-based projects and their perspectives
Section B: AI-based GEE tool and technologies
6. A comprehensive review of emerging AI-based Machine and Deep learning algorithms for GEE
7. Comparative Analysis of various Machine and Deep learning classification algorithms
8. Estimation of land-use land-cover variations using GEE and AI-based change detection tools
9. Monitoring and mapping of urban development with integration of GEE and AI
10. Image fusion of optical and microwave satellite datasets using deep neural networks
11. AI-driven cloud-based remote sensing for big data analysis
Section C: Emerging applications and case studies of GEE in earth observation
12. Remote sensing for Water resource management with GEE
13. Agriculture mapping for crop monitoring using remote sensing and GEE
14. Mapping and monitoring of forest resources and activities using GEE
15. Response to climate change using AI and cloud computing platforms
16. Role of GEE in natural hazard monitoring and management
17. Estimation of Snow/ice cover parameters using GEE and AI
Section D: Challenges and future trends of GEE
18. Challenges and limitations of the cloud-based platforms
19. Futuristic AI-driven tools and technologies for earth observation data analytics
20. Exploration of the science of remote sensing and GIS with Google Earth Engine
21. Creative integration of GEE with AI for algorithms to applications