About this book:

*Big Data from Space* provides a comprehensive framework for transforming raw satellite telemetry into commercially viable analytics. The book begins by detailing the technical foundations of Earth observation, including sensor physics (optical, SAR, thermal), orbital mechanics, and the necessity of cloud-native architectures. It emphasizes the importance of modern standards like SpatioTemporal Asset Catalogs (STAC) and Cloud-Optimized GeoTIFFs (COG) to ensure interoperability and efficient data handling within cloud environments such as AWS, Azure, and Google Cloud.

The core of the book focuses on the end-to-end data pipeline, moving from scalable ingestion and automated preprocessing—covering radiometric, atmospheric, and geometric corrections—to advanced feature engineering and machine learning. It provides practical guidance on building robust models for classification, segmentation, and object detection, while highlighting the critical role of MLOps in managing experiment tracking, model versioning, and drift detection. Specialized chapters on time-series analysis and multi-source fusion (integrating satellite data with AIS, ADS-B, and IoT signals) demonstrate how to extract deep temporal narratives from orbital data.

To ground these technical concepts in business reality, the book features detailed case studies in agriculture, insurance, and logistics. These sections explore high-value applications such as crop yield forecasting, parametric insurance triggers, and global trade flow monitoring. The text also addresses the commercial side of the industry, offering strategies for product design, API development, and various monetization models, ranging from data subscriptions to "Insights-as-a-Service."

Finally, the book examines the operational and ethical dimensions of building an Earth observation business. It covers performance and cost optimization to manage the high "total cost of ownership" associated with spatial data, alongside rigorous validation and uncertainty quantification. The concluding chapters provide a roadmap for team building and organizational structure, while stressing the vital importance of data governance, privacy, and the ethical responsibility to mitigate algorithmic bias in global monitoring systems.

What You'll Find Inside:

• Learn to design scalable ingestion pipelines that index satellite scenes as they arrive, enabling efficient event-driven processing and STAC-based cataloging.
• Master preprocessing techniques including radiometric, atmospheric, and geometric corrections to transform raw satellite data into reliable, analysis-ready datasets.
• Build and productionize machine learning models for imagery classification, segmentation, and detection using MLOps practices for experiment tracking, versioning, and continuous delivery.
• Explore real-world case studies in agriculture, insurance, and logistics to see how satellite data drives decisions on crop yield, catastrophe response, and global trade flows.
• Implement data governance, cost optimization, and ethical frameworks to ensure compliance, scalability, and responsible use of satellite analytics in business applications.

Who's It For:

This book is intended for startup founders validating new Earth observation products, enterprise teams modernizing geospatial stacks, and analysts seeking to move beyond prototypes into reliable services. Readers should possess foundational skills in Python, SQL, and cloud console operations. By following the practical guidance, you will gain the ability to ingest, clean, analyze, and monetize satellite-derived datasets with confidence, ship customer-facing APIs and dashboards, and scale operations without sacrificing scientific rigor.