Part 1: Modern EO Processing Landscape

The Challenge: EO Data Growth¶

Earth Observation is experiencing unprecedented data growth:

The 4 V’s of EO Big Data:

• Volume: Petabytes of new satellite data daily
• Velocity: Near real-time processing requirements
• Variety: Multiple sensors, formats, and resolutions
• Veracity: Quality assessment and validation needs

Traditional vs. Cloud-Native Processing:

What Makes Cloud-Native EO Processing Possible¶

Modern EO workflows depend on a few key innovations working together:

• Packaged Software: Applications that can run anywhere - your laptop, university servers, or cloud platforms
• Scalable Computing: Automatically adding more processing power when needed, like having extra workers during busy times
• Cloud Storage: Data stored on the internet that any computer can access directly, eliminating downloads
• Cloud-friendly Data Formats: Files organized so you can read just the parts you need, like reading one chapter instead of an entire book

The Key Insight: These technologies work together to make data processing feel like using a web browser - instant access to information without worrying about where it’s stored or how much computing power you need.

Data Cube: Organizing EO Data Across Spatial, Temporal, and Spectral Dimensions¶

In Earth Observation (EO), data is inherently multidimensional, spanning space, time, and the electromagnetic spectrum. To efficiently manage and analyze such complex, large-scale datasets, the concept of the data cube is essential.

A data cube is a structured, multidimensional array that organizes EO data along several key dimensions:

• Spatial Coordinates (X, Y): Represent the geographic location using longitude and latitude.
• Temporal Dimension (Time): Captures changes over time, enabling time series analyses and monitoring.
• Spectral Dimensions (Bands): Represent the different wavelengths or spectral bands collected by sensors.
• Attributes: Include metadata, quality indicators, and other descriptive information that adds context and reliability to the data.

This multi-dimensional organization facilitates scalable cloud-native processing, supports advanced analyses, and underpins modern EO workflows that aim to unlock the full potential of satellite data for applications in environmental monitoring, climate science, and resource management.