Google Earth Engine and Artificial Intelligence for Earth Observation

Auteur
Dr. Sood received her B.Tech. degree in Electronics and Communication Engineering from Himachal Pradesh University, Shimla, India, in 2008; M.Tech. degree from I. K. Gujral Punjab Technical University, India, in 2011; and a PhD degree in Electronics and Communication Engineering from Chitkara University, India, in 2020. Her research interests include deep learning, machine learning, and digital image processing. 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.Dr. Singh received his B.Tech., M.Tech., and PhD degrees in Electronics and Communication Engineering from I. K. Gujral Punjab Technical University, India, in 2009, 2011, and 2018, respectively. Since 2012, he has been an Associate Professor with the Dept. of Electronics and Communication Engineering. He is also a visiting researcher (Teacher Associateship for Research Excellence Fellowship awardee, Science and Engineering Research Board) with the Department of Civil Engineering, Indian Institute of Technology (IIT) Ropar, Rupnagar, India. His research interests include electronic sensors; remote sensing of agriculture and digital image processing, in particular, scatterometer, classification, and data fusion.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.

Texte du rabat
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.

Contenu

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