Deep Learning for Multi-Sensor Earth Observation

• Addresses the problem of unwieldy datasets from multi-sensor observations, applying Deep Learning to multi-sensor data integration from disparate sources with different resolution and quality
• Provides a thorough foundational reference to Deep Learning applications for handling Earth Observation multi-sensor data across a variety of geosciences
• Includes case studies and real-world data/examples allowing readers to better grasp how to put Deep Learning techniques and methods into practice

Sudipan Saha is currently an Assistant Professor at Yardi School of Artificial Intelligence, Indian Institute of Technology (IIT) Delhi, New Delhi, India. Previously, he worked as a postdoctoral researcher at the Artificial Intelligence for Earth Observation (AI4EO) Lab, Technical University of Munich, Germany (2020-2022). He received a Ph.D. degree in Information and Communication Technologies from the University of Trento and Fondazione Bruno Kessler (FBK), Trento, Italy in 2020, working with Dr. Francesca Bovolo and Prof. Lorenzo Bruzzone. He is the recipient of FBK Best Student Award 2020. Previously, he obtained the M.Tech. degree in Electrical Engineering from IIT Bombay, Mumbai, India in 2014 where he is recipient of Postgraduate Color. He worked as an Engineer with TSMC Limited, Hsinchu, Taiwan, from 2015 to 2016. His research interests are related to multi-temporal and multi-sensor satellite image analysis, uncertainty quantification, deep learning, and climate change.