Call for Chapters: Generative Adversarial Networks for Remote Sensing

Editors

Amol Vibhute, Symbiosis International (Deemed University), Pune, India
Rajesh Kumar Dhanaraj, Symbiosis International (Deemed University), Pune, India
Karbhari Kale, Dr. Babasaheb Ambedkar Technological University, Lonere, India

Call for Chapters

Proposals Submission Deadline: June 2, 2024
Full Chapters Due: September 15, 2024
Submission Date: September 15, 2024

Introduction

Recently, generative adversarial networks (GANs) are a prominent approach to solving real-time problems. Several domains, like remote sensing, are challenging to handle with conventional methods. The remotely sensed dataset has multiple dimensions that need analysis for better decision-making and future predictions. However, the traditional techniques must be revised to analyse the complex remote sensing data, leading to confused outcomes. In this case, remotely sensed datasets can successively be fused and analysed to address complex challenges in geospatial data and their applications using generative adversarial networks with high success rates. Therefore, the proposed book “Generative Adversarial Networks for Remote Sensing” emphasises the foundations of recent trends in generative adversarial networks and remote sensing applications. Firstly, the book will provide insights into the fundamentals of generative adversarial networks, historical advancements, novel GAN architectures and challenges in analysing remote sensing data using GANs. The book will also focus on feature extraction, object detection and segmentation from remotely sensed data with GANs to interpret geospatial surface features. Subsequently, the book will focus on practical executions of GANs for the time series analysis of remote sensing data for detecting the real-time changes in geolocations, fusion of multiple remote sensing datasets, and processing multi-model data. The book will also focus on real-time case studies on remote sensing applications for precision agriculture, environmental changes and monitoring, smart cities and their planning, and forestry applications using the GANs across varied fields. Lastly, the book will focus on the challenges, ethical issues and considerations, opportunities, privacy-security considerations and adversarial attacks in using the GANs for remote sensing data. In addition, the book will provide a way to collaborate for research, open access datasets, and future directions for researchers, students, and practitioners to empower the use of generative adversarial networks in remote sensing applications to gain insight into the geospatial world for better decision-making and sustainability.

Objective

The proposed book has the following primary objectives: 1) To develop novel data augmentation techniques using GANs to generate synthetic remote-sensing images. 2) To investigate using GANs for image translation tasks in remote sensing, such as translating images from one spectral band to another or enhancing spatial resolution. 3) To develop GAN-based anomaly detection models and change detection models for detecting the changes. 4) To develop GAN architectures for semantic segmentation and object detection tasks in remote sensing imagery. 5) To investigate GANs for uncertainty quantification and robustness analysis in remote sensing applications. 6) To Explore methods for enhancing the interpretability and explainability of GAN-generated remote sensing imagery. 7) To develop GAN models capable of learning domain-invariant representations that improve the transferability of deep learning models across diverse remote sensing datasets.

Target Audience

Researchers, Students, Practitioners, Decision-makers, Land planners, industries, and Governments.

Recommended Topics

1. Foundations of generative artificial intelligence: An overview of generative adversarial networks and historical advancements 2. The architectures and types of generative adversarial networks: Training challenges 3. Generative adversarial networks in feature engineering: An unsupervised approach 4. Generative models for remote sensing data augmentation and challenges 5. Use of generative adversarial networks in remote sensing and its applications: An overview and motivations 6. Remote sensing data and applications with generative adversarial networks 7. Generative adversarial networks in object detection and segmentation with remote sensing images 8. Generative adversarial networks in time series analysis and change detections using remote sensing imagery 9. Transfer learning with generative adversarial networks: pretrained and fine-tuning approaches in remote sensing applications 10. Generative adversarial networks in remote sensing data fusion and processing multi-model data 11. Challenges, ethical issues, considerations, and opportunities in using generative adversarial networks to remote sensing data 12. Privacy-security considerations and adversarial attacks on remote sensing with GANs 13. Case studies on generative adversarial networks in precision farming 14. Case studies on generative adversarial networks in smart cities and plannings 15. Case studies on generative adversarial networks in forestry applications 16. Case studies on generative adversarial networks in environmental changes 17. Generative adversarial networks integration with remote sensing and GIS 18. Generative adversarial networks for remote sensing: future trends and directions 19. Research collaborations and open access datasets for generative adversarial networks in remote sensing

Submission Procedure

Researchers and practitioners are invited to submit on or before June 2, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 16, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by September 15, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Generative Adversarial Networks for Remote Sensing. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

June 2, 2024: Proposal Submission Deadline
June 16, 2024: Notification of Acceptance
September 15, 2024: Full Chapter Submission
November 17, 2024: Review Results Returned
December 29, 2024: Final Acceptance Notification
January 12, 2025: Final Chapter Submission



Inquiries

Amol Vibhute Symbiosis International (Deemed University), Pune amolvibhute2011@gmail.com Rajesh Kumar Dhanaraj Symbiosis International (Deemed University), Pune sangeraje@gmail.com Karbhari Kale Dr. Babasaheb Ambedkar Technological University, Lonere kvkale91@gmail.com

Classifications


Computer Science and Information Technology; Environmental, Agricultural, and Physical Sciences; Security and Forensics; Science and Engineering
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