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Elsevier

Fundamentals of Next-Generation Remote Sensing

Data-Driven Earth Observation, Artificial Intelligence, and Emerging Applications

  • 1st Edition - April 1, 2027
  • Latest edition
  • Editors: Xiao Huang, Qihao Weng, Elif Sertel, Jungho Im
  • Language: English

Fundamentals of Next-Generation Remote Sensing: Data-Driven Earth Observation, Artificial Intelligence, and Emerging Applications addresses the urgent need for a compre… Read more

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Description

Fundamentals of Next-Generation Remote Sensing: Data-Driven Earth Observation, Artificial Intelligence, and Emerging Applications addresses the urgent need for a comprehensive, practice-oriented resource in the rapidly evolving field of Earth observation. As sensor technologies advance and data volumes grow exponentially, researchers and professionals require a cohesive guide that bridges traditional image processing with cutting-edge AI, cloud computing, and participatory paradigms. The book covers sourcing multi-sensor imagery—from satellites, drones, and in-situ networks—automating data harmonization, training state-of-the-art AI models, and deploying interactive dashboards for decision-makers. Its chapters span foundational data ecosystems, sensor technologies, pre-processing workflows, AI architectures, and high-impact applications such as disaster response, climate analysis, urban planning, and biodiversity monitoring. This volume offers a unique synthesis of technical workflows, open-source tools, and ethical considerations, making it invaluable for remote sensing scientists, geospatial data engineers, urban planners, and environmental policymakers. It empowers users to develop scalable, reproducible, and policy-relevant solutions, ensuring that remote sensing data transforms into actionable insights for societal benefit.

Whether for research, operational deployment, or education, this book provides the essential pipelines, case studies, and best practices needed to keep pace with the advancing Earth observation landscape.

Key features

  • Surveys cutting-edge sensors, AI breakthroughs, and geospatial workflows
  • Explains multi-paradigm AI techniques, from foundation models to low-code tools
  • Embeds ethics, uncertainty, and participatory workflows throughout the content
  • Provides open-source code snippets, workflow diagrams, and case studies
  • Equips users with scalable, reproducible, and policy-relevant remote sensing methods

Readership

Remote sensing and Earth observation professionals, Geospatial data scientists, Urban planners, Environmental scientists

Table of contents

Part I. Foundations & Data Ecosystem

1. A New Era of Remote Sensing: From Pixels to Pipelines

2. The Modern Remote-Sensing Data Landscape

3. Open-Source Toolchains That Power RS at Scale

Part II. Data Acquisition & Sensor Technologies

4. Satellite Constellations & Smallsat Swarms

5. Airborne & UAV Imaging

6. LiDAR, SAR & Hyperspectral Advances

7. In-situ & IoT Sensor Fusion

Part III. Pre-Processing & Harmonization

8. Automated Data Ingestion & Harmonization

9. Radiometric & Atmospheric Correction with ML

10. Geometric Correction & Sub-pixel Co-registration

Part IV. Artificial Intelligence Methods

11. Deep Learning Architectures for RS

12. Self-Supervised & Foundation Models

13. Temporal Modeling & Change Detection

14. Graph & Geo-Spatial Deep Learning

15. Explainable AI & Uncertainty Quantification

Part V. High-Impact Applications

16. Land-Cover & Land-Use Mapping 2.0

17. Rapid Disaster Mapping & Response

18. Climate & Carbon Analytics

19. Urban Analytics

20. Agriculture & Food Security

21. Biodiversity & Ecosystem Health

Part VI. Pipeline Orchestration & Deployment

22. Workflow Orchestration & Cloud Scaling

23. Ethical, Legal & Societal Considerations

24. Interactive Visualization & API Delivery

Part VII. Emerging Trends and Future Perspectives

25. Generative AI for Remote-Sensing Content Creation

26. Synthetic Data & Digital-Twin Simulators

27. Citizen-Centric Remote Sensing & Participatory Mapping

28. Low-Code/No-Code Geospatial-AI Platforms

Product details

  • Edition: 1
  • Latest edition
  • Published: April 1, 2027
  • Language: English

About the editors

XH

Xiao Huang

Professor Xiao Huang is an Assistant Professor at the Department of Environmental Sciences at Emory University. His research primarily focuses on geospatial analysis, geovisualization, environmental modeling, computer and data science, and Big Data analytics. His research takes advantage of and also addresses the challenges of rapidly growing data availability through the utilization and development of advanced data fusion techniques and deep learning algorithms. Dr. Huang has authored/co-authored over 120 refereed publications. He serves as an Associated Editor for Computational Urban Science and sits on the Editorial Board for Big Earth Data, International Journal of Digital Earth, Frontiers Remote Sensing, Nature Scientific Reports, Journal of Remote Sensing, Current Social Sciences, and PLOS ONE. He also serves as a reviewer for NASA and NSF grants and for 49 esteemed scientific journals.

Affiliations and expertise
Assistant Professor, Department of Environmental Sciences, Emory University, USA

QW

Qihao Weng

Qihao Weng is a Chair Professor at The Hong Kong Polytechnic University and a Foreign Member of the Academia Europaea. A Fellow of IEEE, AAAS, AAG, ASPRS, and AAIA, he previously directed Indiana State University’s Center for Urban and Environmental Change (2001-2021) and served as a NASA Senior Fellow (2008-2009). He led the GEO Global Urban Observation & Information Initiative (2012-2022) and is Editor-in-Chief of the ISPRS Journal of Photogrammetry and Remote Sensing. He also edits two Taylor & Francis book series in remote sensing and imaging science, chairs the IEEE/ISPRS/GEO International Workshop on Earth Observation series (since 2008), and has held leadership roles with ASPRS and the U.S. DOE.

Affiliations and expertise
Chair Professor, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong

ES

Elif Sertel

Elif Sertel is Professor of Geomatics Engineering at Istanbul Technical University (ITU). Her research explores how land-cover change and urbanization affect regional climate. Sertel joined ITU’s faculty in 2009, became Associate Professor in 2010, and Professor in 2016. She directed ITU’s Center for Satellite Communications and Remote Sensing (2012–2021) after serving as Vice-Director (2009–2012) and now evaluates EU research proposals as Vice-Chair and expert. Honors include the Turkish Academy of Sciences Outstanding Young Scientist Award (TÜBA-GEBİP, 2017) and the TÜBİTAK Engineering Incentive Award (2021), making her the first Turkish geomatics engineer to receive both distinctions.

Affiliations and expertise
Professor of Geomatics Engineering, Istanbul Technical University (ITU), Istanbul, Turkey

JI

Jungho Im

Jungho Im is a professor and the director of the Intelligent Remote Sensing and Geospatial Information Science (IRIS) Lab within the Department of Civil, Urban, Earth, and Environmental Engineering at the Ulsan National Institute of Science and Technology (UNIST), South Korea. His research primarily focuses on environmental remote sensing and artificial intelligence modeling, emphasizing disaster monitoring and prediction. His work aims to broaden and deepen the scientific understanding of Earth's systems under conditions of climate variability and change. He has published extensively in prestigious international journals and actively contributes to the scientific community through editorial roles. He currently serves as Editor-in-Chief of GIScience and Remote Sensing and as Associate Editor of the ISPRS Journal of Photogrammetry and Remote Sensing.
Affiliations and expertise
Department of Civil, Urban, Earth, and Environmental Engineering UNIST (Ulsan National Institute of Science & Technology), Korea