Generative Adversarial Networks for Remote Sensing

Amol Dattatraya Vibhute

ISBN 13: 9798369369012
Published by IGI Global Apr 2025, 2025
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Generative adversarial networks (GANs) are transforming the way complex remote sensing data is analyzed, offering innovative solutions for geospatial applications. Traditional methods often struggle to process high-dimensional remotely sensed datasets, leading to limitations in decision-making and predictive accuracy. By leveraging GANs, researchers can enhance feature extraction, object detection, and time-series analysis, enabling more precise environmental monitoring, urban planning, and agricultural assessments. This technological advancement not only improves real-time geospatial analysis but also opens new avenues for interdisciplinary collaboration, ethical considerations, and security challenges in AI-driven remote sensing. As GANs continue to evolve, their application in remote sensing holds the potential to drive sustainability and more informed global decision-making. Generative Adversarial Networks for Remote Sensing emphasizes the foundations of recent trends in GANs and remote sensing applications. It provides insights into the fundamentals of generative adversarial networks, historical advancements, novel GAN architectures and challenges in analyzing remote sensing data using GANs. Covering topics such as change detection, resource management, and feature engineering, this book is an excellent resource for geographers, geospatial data analysts, engineers, professionals, researchers, scholars, academicians, and more.

About the Authors: Amol D. Vibhute received his PhD in Computer Science under the domain of Geospatial Technology, M. Phil and M. Sc. in Computer Science from the Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Chhatrapati Sambhaji Nagar (formarly known as Aurangabad), MH-India. He is an Assistant Professor (Senior Scale) and Program Head of M. Sc. (CA), MBA (IT), and MBA (DT) courses at the Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, Maharashtra, India. He has more than 10+ years of academic experience in research, innovation, and UG/PG-level teaching. His six (06) Indian patents are under the granting stage, two (02) International and one (01) national patent have been granted, and he authored/co-authored over 85+ referred journals, book chapters, and conference papers in reputed international journals and conferences indexed by Scopus/SCIE/UGC. The current publication citations of his research are more than 1250+, 700+, and 350+, with an h-index of 17, 12, and 7 as per Google Scholar, Scopus, and Web of Science, respectively. His research interests include Geospatial Technology, Digital Image Processing, Pattern Recognition, Big Data Analysis, the Internet of Things (IoT), Machine Learning, and Artificial Intelligence. He is an ISPRS, IAEng, CSTA, IACSIT, and ISCA member. He is an editorial board member for Scientific Reports (Sci Rep) published by “Nature Portfolio” ISSN: 2045-2322 (online). Also, he is an active reviewer of the Taylor and Francis, Elsevier, Wiley Online, IEEE Access and Springer journals indexed by SCI, SCIE, and Scopus.

K. V. Kale is currently working as a Vice-Chancellor of Dr Babasaheb Ambedkar Technological University, Lonere, Raigad, India. He worked as a Professor, Program Coordinator of UGC SAP and DST FIST, in the Department of Computer Science and IT as well as Coordinator of University RUSA project & School of Computational and Physical Sciences. He worked as a Director, Board of College and University Development (BCUD) of Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra. He was Head of the Department of Computer Science and IT at University more than 9 years. He also served as Director of University Network & Information Center (UNIC) of Dr. BAMU. He has acted as President of ICT Section the Indian Science Congress Association, Kolkata in the year 2015-2016 under the Ministry of Science & Technology, Government of India. He is a pioneer of Multimodal Biometrics and Remote Sensing Data Analysis in the region and established & developed state-of-art Multimodal Biometrics Research Laboratory (MBRL) as a unique national Laboratory, RS and GIS, Pattern Recognition and as a center of excellence in teaching and research in biometrics and advance Image processing technology in university. He has more than 30 years of experience in research, innovation and teaching at UG, PG and PhD level. He has published more than 350+National and International research articles, 06 books and 23 book chapters. The current total impact factor of his research is more than 24, with h-index of 25 and i10-index of 57 and 8 Indian Patent 2- Australian patents. He has given a number of plenary lectures, invited talks and Chaired Sessions at Symposia, workshop, national &international conferences. He has successfully supervised 36 Ph.D., 08 M. Phil and 22M.Tech students and currently 07 PhD students, 06 M.Phil. Students and 14M. Tech (CSE) scholars are working with him. His area of research includes Remote Sensing and GIS, Image Processing, Pattern Recognition, Computer Vision, Software Engineering, Artificial Intelligence, Neural Network, Big Data Analytic, Internet of Things (IoT) etc. He is Editor in Chief, Editors, Reviewers of various International, National Journals. Professor Kale has several academic honours and professional distinctions to his credits. He has served on various bodies and authorities of the university like Senate Member, Management Council Member, Academic Council Member, BOS & RRC’s chairman, BUTER Member, BCUD member, etc. time to time. He has also been working on various statutory authorities of other universities, in different capacities such as; Chairman and Member.

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Title: Generative Adversarial Networks for Remote ...
Publisher: IGI Global Apr 2025
Publication Date: 2025
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Paperback. Condition: new. Paperback. Generative adversarial networks (GANs) are transforming the way complex remote sensing data is analyzed, offering innovative solutions for geospatial applications. Traditional methods often struggle to process high-dimensional remotely sensed datasets, leading to limitations in decision-making and predictive accuracy. By leveraging GANs, researchers can enhance feature extraction, object detection, and time-series analysis, enabling more precise environmental monitoring, urban planning, and agricultural assessments. This technological advancement not only improves real-time geospatial analysis but also opens new avenues for interdisciplinary collaboration, ethical considerations, and security challenges in AI-driven remote sensing. As GANs continue to evolve, their application in remote sensing holds the potential to drive sustainability and more informed global decision-making. Generative Adversarial Networks for Remote Sensing emphasizes the foundations of recent trends in GANs and remote sensing applications. It provides insights into the fundamentals of generative adversarial networks, historical advancements, novel GAN architectures and challenges in analyzing remote sensing data using GANs. Covering topics such as change detection, resource management, and feature engineering, this book is an excellent resource for geographers, geospatial data analysts, engineers, professionals, researchers, scholars, academicians, and more. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798369369012

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Paperback. Condition: new. Paperback. Generative adversarial networks (GANs) are transforming the way complex remote sensing data is analyzed, offering innovative solutions for geospatial applications. Traditional methods often struggle to process high-dimensional remotely sensed datasets, leading to limitations in decision-making and predictive accuracy. By leveraging GANs, researchers can enhance feature extraction, object detection, and time-series analysis, enabling more precise environmental monitoring, urban planning, and agricultural assessments. This technological advancement not only improves real-time geospatial analysis but also opens new avenues for interdisciplinary collaboration, ethical considerations, and security challenges in AI-driven remote sensing. As GANs continue to evolve, their application in remote sensing holds the potential to drive sustainability and more informed global decision-making. Generative Adversarial Networks for Remote Sensing emphasizes the foundations of recent trends in GANs and remote sensing applications. It provides insights into the fundamentals of generative adversarial networks, historical advancements, novel GAN architectures and challenges in analyzing remote sensing data using GANs. Covering topics such as change detection, resource management, and feature engineering, this book is an excellent resource for geographers, geospatial data analysts, engineers, professionals, researchers, scholars, academicians, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798369369012

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Hardcover. Condition: new. Hardcover. Generative adversarial networks (GANs) are transforming the way complex remote sensing data is analyzed, offering innovative solutions for geospatial applications. Traditional methods often struggle to process high-dimensional remotely sensed datasets, leading to limitations in decision-making and predictive accuracy. By leveraging GANs, researchers can enhance feature extraction, object detection, and time-series analysis, enabling more precise environmental monitoring, urban planning, and agricultural assessments. This technological advancement not only improves real-time geospatial analysis but also opens new avenues for interdisciplinary collaboration, ethical considerations, and security challenges in AI-driven remote sensing. As GANs continue to evolve, their application in remote sensing holds the potential to drive sustainability and more informed global decision-making. Generative Adversarial Networks for Remote Sensing emphasizes the foundations of recent trends in GANs and remote sensing applications. It provides insights into the fundamentals of generative adversarial networks, historical advancements, novel GAN architectures and challenges in analyzing remote sensing data using GANs. Covering topics such as change detection, resource management, and feature engineering, this book is an excellent resource for geographers, geospatial data analysts, engineers, professionals, researchers, scholars, academicians, and more. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798369369005

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