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Full Description
This book explores the cutting-edge integration of generative AI techniques to enhance environmental remote sensing, providing a comprehensive guide from foundational algorithms to practical applications. It explains how advanced AI technology can be used to improve the way we monitor and understand the environment from a distance, such as through satellites or drones. It starts with an explanation of basic algorithms behind generative AI and gradually moves to complex algorithms showing how they can be applied to real-world environmental issues, such as tracking climate change, monitoring deforestation, and predicting natural disasters.
Features
Includes real-world examples and case studies showing how generative AI improves environmental monitoring.
Provides step-by-step explanations of algorithms and their implementation.
Explains complex concepts in simple and easy-to-understand language and introduces strategies to address environmental challenges using AI-driven solutions.
Offers cutting-edge research and advancements in AI and remote sensing including the application of generative AI models like GANs and VAEs.
Applies to diverse fields such as urban planning, agriculture, and disaster management.
Discuses ethical considerations and challenges when integrating AI with remote sensing.
Generative AI for Remote Sensing of the Environment: Algorithms and Applications is for researchers and practitioners in environmental monitoring, urban planning, agriculture, and disaster management using remote sensing technologies and AI to address environmental challenges and sustainability. As well as university professors, graduate, and postgraduate students in environmental science, geospatial analysis, computer science, and data science working on projects related to AI and remote sensing.
Contents
Section A: Introduction to Gen AI. 1. Introduction To Generative AI And Its Role in Remote Sensing. 2. Core Concepts of Understanding Generative AI Algorithms and Models. 3. The Use of Generative AI in Environmental Remote Sensing. 4. Vision-Language Models in Remote Sensing: Balancing Geospatial Intelligence with Ethical and Responsible AI Practices. 5. Advanced Large Language Models for Satellite Image Processing. 6. Tools and Software Essential Resources for AI Integration. Section B: Applications and Case Studies of Gen AI in Remote Sensing. 7. Remote Sensing Satellite Datasets, Preprocessing Techniques, and Tools for Agricultural Land Cover Classification. 9. Implementing Artificial Intelligence-Based Algorithms for Sustainable Environmental Monitoring. 10. Data Preparation, Collecting, Cleaning, and Managing Datasets in Generative AI. 11. High-Resolution Soil Erosion Mapping for the Narmada Basin: A RUSLE-Google Earth Engine Synergy. 12. AI Integration in Agriculture: Tools, Software, and Frameworks for Sustainable Farming. 13. Agriculture Using Generative AI for Crop Management. Section C: Resources, Challenges, and the Future of Gen AI in Remote Sensing. 14. Harnessing AI to Unveil the Future: Modelling and Forecasting Climate Change Effects. 15. Real-World Applications, Success Stories, and Industry Insights. 16. Challenges with Practical Solutions and Case Studies. 17. Future Trends Innovations in AI and Remote Sensing.
