Google Earth Engine and Artificial Intelligence for Earth Observation
Algorithms and Sustainable Applications
Series: Earth Observation;
- Publisher's listprice EUR 139.99
-
58 060 Ft (55 296 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 11 612 Ft off)
- Discounted price 46 449 Ft (44 237 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
58 060 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Elsevier Science
- Date of Publication 9 June 2025
- ISBN 9780443273728
- Binding Paperback
- No. of pages576 pages
- Size 229x152 mm
- Weight 1000 g
- Language English 669
Categories
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. 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.
MoreTable of Contents:
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
