Explainable AI for Earth Observation Data Analysis by Arun PV (.ePUB)+

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File Size: 38.7 MB

Explainable AI for Earth Observation Data Analysis by Arun PV, Jocelyn Chanussot, B Krishna Mohan, D Nagesh Kumar, Alok Porwal
Requirements: .ePUB, .PDF reader, 38.7 MB
Overview: The role of Artificial Intelligence (AI) is crucial in the domain of Earth Observation (EO) data analysis. Deep Learning-based approaches have improved accuracy, but they have affected the reliability and transparency of EO data. It is critical to improve the explainability of EO data analysis algorithms and complex Deep Learning models to ensure the quality of spatial decisions. This book discusses the various advancements in Explainable AI and investigates their suitability for various EO data analyses offering best practices for implementing algorithms that facilitate big and efficient data processing. It lays the foundation of Explainable EO and helps readers build trustworthy, secure, and robust EO systems. By leveraging Big Data analysis technologies such as cloud computing, Machine Learning (ML), and Artificial Intelligence (Al), scientists and decision-makers can extract valuable insights for climate monitoring, disaster response, urban planning, and more. However, although the rapid evolution of Al/ML has unlocked unprecedented capabilities to analyze these data, the trade-off between model accuracy and transparency poses significant challenges for deploying Al systems in sensitive, mission-critical applications. All Deep Learning analyses were carried out in Python. This book is intended for graduate students, researchers and academics in computer and Data Science, Machine Learning, and image processing, as well as professionals in geospatial Data Science using GIS and remote sensing in Earth and environmental sciences.
Genre: Non-Fiction > Tech & Devices

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