Kommande
Data & IT
Pocket
Remote Sensing, Big Data, and GeoAI
Erin Bunting • Jane Southworth • Cerian Gibbes • Hannah Herrero
2059:-
Remote Sensing, Big Data, and GeoAI: Exploring Applications with Geospatial Insights is an in-depth analysis of the transformative power of AI and Big Data in remote sensing. This book provides readers with the knowledge and tools to utilize these technologies to enhance decision-making and analysis. Starting from fundamental concepts, it progresses to advanced applications, offering accessible explanations and real-world examples to bridge the gap between theory and practice.
The book uses a structured format to balance theoretical knowledge with immersive case studies, giving readers a deeper understanding of practical implications.
It also covers ethical and legal considerations, making it an invaluable resource for researchers, professionals, and students keen on using AI and Big Data techniques in remote sensing to solve complex geospatial challenges.
The book uses a structured format to balance theoretical knowledge with immersive case studies, giving readers a deeper understanding of practical implications.
It also covers ethical and legal considerations, making it an invaluable resource for researchers, professionals, and students keen on using AI and Big Data techniques in remote sensing to solve complex geospatial challenges.
- Provides a full review of the development of Big Data and AI for remote sensing technologies, elucidating on how we have begun to incorporate data and methods, and highlighting potential areas of growth
- Approaches the content from a practical but immersive angle, allowing readers to understand common data and methodological approaches with real world examples
- Includes chapters on emerging topics of consideration, including the ethics/legality of using AI in remote sensing and how we teach such complex topics to students
- Format: Pocket/Paperback
- ISBN: 9780443267413
- Språk: Engelska
- Antal sidor: 375
- Utgivningsdatum: 2026-03-01
- Förlag: Elsevier Science