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.
- 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
