Google Earth Engine and Artificial Intelligence for Earth Observation (eBook, PDF)

Algorithms and Sustainable Applications
Redaktion: Sood, Vishakha; Pradhan, Biswajeet; Singh, Sartajvir; Gupta, Dileep Kumar

• Format: PDF

Produktbeschreibung

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. It includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. This book 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. Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications 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 of scientific domains in remote sensing and geographic information systems (GIS) including 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 - Includes detailed case studies showing specific considerations and exceptions for applications of AI in GEE for Earth observation

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 390
• Erscheinungstermin: 28. März 2025
• Englisch
• ISBN-13: 9780443273735
• Artikelnr.: 73756945

Inhaltsangabe

Section A - Introduction of AI-driven GEE cloud computinge
based remote sensing
1. Introduction to Google Earth Engine: A comprehensive workflow
2. Role of GEE in earth observation via remote sensing
3. A meta-analysis of Google Earth Engine in different scientific domains
4. Exploration of science of remote sensing and GIS with GEE
5. Cloud computing platformsebased remote sensing big data applications
6. Role of various machine and deep learning classification algorithms in
Google Earth Engine: A comparative analysis
7. Google Earth Engine and artificial intelligence for SDGs
Section B - Emerging applications of GEE in Earth observation
8. Machine learning algorithms for air quality and air pollution monitoring
using GEE
9. Investigation of surface water dynamics from the Landsat series using
Google Earth Engine: A case study of Lake Bafa
10. Monitoring of land cover changes and dust events over the last 2
decades using Google Earth Engine: Hamoun wetland, Iran
11. Leveraging Google Earth Engine for improved groundwater management and
sustainability
12. Customized spatial data cube of urban environs using Google Earth
Engine (GEE)
13. A novel self-supervised framework for satellite image classification in
the Google Earth Engine cloud computing platform
14. Assessment and monitoring of forest fire using vegetation indices and
AI/ML techniques over google earth engine
15. Utilizing google earth engine and remote sensing with machine learning
algorithms for assessing carbon stock loss and atmospheric impact through
pre- and postfire analysis
16. Time series of Sentinel-1 and Sentinel-2 imagery for parcel-based
crop-type classification using Random Forest algorithm and Google Earth
Engine
17. Multi-temporal monitoring of impervious surface areas (ISA) changes in
an Arctic setting, using ML, remote sensing data, and GEE
18. Estimation of snow or ice cover parameters using Google Earth engine
and AI
19. Climate change challenges: The vital role of Google Earth Engine for
sustainability of small islands in the archipelagic countries
20. Evaluating machine learning algorithms for classifying urban
heterogeneous landscapes using GEE
21. Application of analytic hierarchy process for mapping flood
vulnerability in Odisha using Google Earth Engine
22. Deep learning-based method for monitoring precision agriculture using
Google Earth Engine
23. Role of AI and IoT in agricultural applications using Google Earth
Engine
24. Mature and immature oil palm classification from image Sentinel-2 using
Google earth engine (GEE)
25. Tracking land use and land cover changes in Ghaziabad district of India
using machine learning and Google Earth engine
Section C - Challenges and future trends of GEE
26. Challenges and limitations for cloud-based platforms and integration
with AI algorithms for earth observation data analytics
27. AI-driven tools and technologies for agriculture land use & land cover
classification using earth observation data analytics