Advances in Machine Learning and Image Analysis for Geoai

Advances in Machine Learning and Image Analysis for GeoAI presents recent advances in applications and algorithms that are at the intersection of Geospatial imaging and Artificial Intelligence (GeoAI). The book covers algorithmic advances in geospatial image analysis, sensor fusion across modalities, few-shot open-set recognition, explainable AI for Earth Observations, self-supervised learning, image superresolution, Visual Question Answering, and spectral unmixing, among other topics.
This book offers a comprehensive resource for graduate students, researchers, and practitioners in the area of geospatial image analysis. It provides detailed descriptions of the latest techniques, best practices, and insights essential for implementing deep learning strategies in GeoAI research and applications.

• Covers the latest machine learning and signal processing techniques that can effectively leverage multimodal geospatial imagery at scale
• Chapters cover a variety of algorithmic frameworks pertaining to GeoAI, including superresolution, self-supervised learning, data fusion, explainable AI, among others
• Presents cutting-edge deep learning architectures optimized for a wide array of geospatial imagery

Saurabh Prasad (Senior IEEE Member) is an Associate Professor with the Department of Electrical and Computer Engineering, University of Houston, USA, where he directs the Machine Learning and Signal Processing Laboratory. His lab focuses on advancing state-of-the-art in machine learning and signal processing with applications to remote sensing and biomedicine. His work has been recognized by two student research awards during his Ph.D. study, a best student paper award at the 2008 IGARSS conference, top-10% papers at IEEE-ICIP conference, a NASA New Investigator (Early Career) award in 2014, and the junior faculty research excellence award at the University of Houston in 2017. He was the lead book editor on two Springer books on signal processing and machine learning for hyperspectral image analysis.

Jocelyn Chanussot (IEEE Fellow) has been with Grenoble INP since 1999, where he is currently a Professor of signal and image processing. His research interests include image analysis, hyperspectral remote sensing, data fusion, machine learning and artificial intelligence. He has been a visiting scholar at Stanford University (USA), KTH (Sweden) and NUS (Singapore) and is an Adjunct Professor of the University of Iceland. In 2015-2017, he was a visiting professor at the University of California, Los Angeles. He holds the AXA chair in remote sensing and is an Adjunct professor at the Chinese Academy of Sciences, Aerospace Information research Institute, Beijing. Dr. Chanussot is the founding President of IEEE Geoscience and Remote Sensing French chapter for which he which received the 2010 IEEE GRS-S Chapter Excellence Award. He was also the Vice-President of the IEEE Geoscience and Remote Sensing Society, in charge of meetings and symposia; the General Chair of the first IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing; Chair and Cochair of the GRS Data Fusion Technical Committee.

Jun Li (IEEE Fellow) is a Professor with the School of Geography and Planning, Sun Yat-sen University, Guangzhou, China. Since 2013, she has obtained several prestigious funding grants at the national and international level. Her research interests include remotely sensed hyperspectral image analysis, signal processing, supervised/semisupervised learning, and active learning. Dr. Li currently serves as the Editor-in-Chief for the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.