Google Earth Engine and Artificial Intelligence for Earth Observation

Algorithms and Sustainable Applications
Erscheinungsjahr 2025
ISBN: 978-0-443-27372-8
Verlag: Elsevier Science

Buch, Englisch, 576 Seiten, Format (B × H): 161 mm x 227 mm, Gewicht: 1000 g

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.

Autoren/Hrsg.

Sood, Vishakha

Gupta, Dileep Kumar

Singh, Sartajvir

Pradhan, Biswajeet

Fachgebiete

• Geowissenschaften Geologie GIS, Geoinformatik
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung

Inhaltsverzeichnis

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

Über Autor(innen)

Sood, Vishakha
Dr. Sood is working as Scientist at Indian Institute of Technology (IIT), Ropar, India, under Women Scientist Scheme (WOS) by Department of Science & Technology (DST), Govt. of India. She is also founder of a company named as Aiotronics Automation Pvt.Ltd. supported under Himachal Pradesh CM Startup Scheme. She has more than 10 years of experience in the field of academics and research. She received her PhD in Electronics and Communication Engineering from Chitkara University, Punjab in 2020. She has done B. Tech from Himachal Pradesh University (HPU) Shimla, 2008 and M. Tech from Punjab Technical University (PTU) in Electronics and Communication Engineering,2011. She has also done MBA in Human Resource (HR) ,2010. She has authored more than 25 SCI-indexed articles (IEEE, T&F, ELSEVIER, and SPRINGER), SCOPUS indexed book chapters and holds many inventions. Her research interests include satellite sensors, remote sensing, scatterometer and digital image analysis.

Gupta, Dileep Kumar
Dileep Kumar Gupta received his doctoral degree from the Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, India. Dr. Dileep received several reputed awards like UGC-NET, GATE, UGC research fellowship and DST international travel support. He has published 30+ research articles in different peer reviewed journals/conference proceedings/book chapters. He is an expert in algorithm development for soil moisture and crop variables retrieval using different ground based and space borne active and passive microwave sensor. He is also an expert of different machine learning algorithms for remote sensing data processing.

Pradhan, Biswajeet
Biswajeet Pradhan is a distinguished professor at UTS School of Civil and Environmental Engineering. He is an international expert in data-driven modelling and a pioneer in combining spatial modelling with statistical and machine learning models for natural hazard predictions including landslides. He has a track record of outstanding research outputs, with over 600 journal articles. He is a highly interdisciplinary researcher with publications across 12 areas, listed as having 'Excellent' international collaboration status. He has been a Highly Cited Researcher for five consecutive years (2016-2020) and ranks fifth in the field of Geological & Geoenvironmental Engineering.

Singh, Sartajvir
He is a digital image analyst with a passion for remote sensing. Presently, he is working as a Professor and Associate Director (University Institute of Engineering) at Chandigarh University, Punjab, India. He is also practice as an Indian Patent Agent (IN/PA 5806). He received his PhD (Electronics and Communication Engineering - ECE) from I.K. Gujral Punjab Technical University, Punjab, India in 2018. He received his M.Tech (ECE) as a Gold Medalist, and B.Tech (ECE) with Distinction, from Punjab Technical University in 2011 and 2009, respectively. His research interests include electronics, remote sensing, and digital image processing.