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WHO USES THE OPEN DATA CUBE?

Scientists and Researchers

  • Streamlines access to large-scale EO datasets

  • Facilitates complex analyses and time series studies

  • Enables reproducible research with standardized data handling

Industry and Businesses

  • Provides valuable insights for agriculture, forestry, and urban planning

  • Enables development of new geospatial products and services

  • Reduces barriers to entry for Earth observation analytics

Government Agencies

  • Supports evidence-based policy making

  • Aids in monitoring environmental changes and natural resources

  • Enhances disaster response and management capabilities

Educators and Students

  • Offers a platform for learning about remote sensing and geospatial analysis

  • Provides free access to real-world data for educational projects

01

Democratizes Access to Earth Observation Data

  • Makes satellite data more accessible and usable for a wider audience

  • Reduces technical barriers to working with complex EO datasets

02

Enhances Decision Making

  • Provides timely, accurate information for environmental monitoring

  • Supports sustainable development and resource management

WHY IS THE OPEN DATA CUBE BENEFICIAL?

03

Performance Optimization​​

  • Lazy evaluation and on-demand processing

  • Caching mechanisms for frequently accessed data

  • Query optimization for efficient data retrieval

04

Promotes Efficiency​​

  • Reduces duplication of effort in data preparation and analysis

  • Standardizes data handling processes across different applications

05

User-friendly Interfaces​​

  • Command-line tools for advanced users and automation

  • Jupyter Notebook integration for interactive analysis

  • Web-based user interface for data discovery and basic analysis

06

Accelerates Scientific Discovery​

  • Enables large-scale, data-intensive research

  • Facilitates cross-disciplinary collaborations

WHAT IS THE OPEN DATA CUBE AND WHAT CAN IT DO?

The Open Data Cube at it's core provides a unified framework for handling vast quantities of satellite imagery and other gridded geospatial information, making it easier to access, process, and derive insights from our planet's wealth of observational data. 

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Geospatial Data:

  • Satellite Imagery

  • UAS Imagery

  • Ground Based Weather Stations

ODC ECOSYSTEM

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ODC CORE

ODC ALGORITHMS

ODC APPS

FLEXIBLE DEPLOYMENT

Depending on your application, the Open Data Cube can be deployed on HPC, Cloud, and local installations. Typical installations run in Linux based environments.

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Informed Decisions:

  • Deforestation

  • Water Quality

  • Illegal Mining

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Firefly an image representing layers of code and mathematics depicting useful connections

Key Features

The Open Data Cube is a powerful platform for managing and analyzing Earth Observation (EO) data at scale. It offers efficient cataloging and organization of vast EO datasets, along with robust metadata management and data provenance tracking. Its Python-based API enables high-performance querying and custom analysis development. The platform supports multi-sensor data integration, flexible data access, and scalable processing capabilities, including cloud deployment and parallel processing for everything from checking out your back yard to continental-scale analyses.

01.

Data Management and Organization

02.

Multi-sensor Data Integration

03.

Flexible Data Access

04.

Scalable Processing

05.

Interoperability

WHY BEING OPEN SOURCE MATTERS?

Transparency and Trust:

  • Open code ensures scientific integrity and reproducibility

  • Allows for community review and improvement of algorithms

Cost-Effective:

  • ​Reduces development costs through shared resources
  • ​Eliminates licensing fees, making it accessible to all

Collaborative Development:

  • Harnesses the collective expertise of a global community

  • Accelerates innovation through shared knowledge

Longevity and Sustainability:

  • Not dependent on a single entity or funding source

  • Ensures continued development and improvement over time

ODC CORE vs ODC STAC

Key considerations:

  1. Data sources: Local/centralized vs. distributed/varied

  2. Interoperability needs: Within organization vs. across multiple platforms

  3. Performance requirements: Optimized for specific datasets vs. flexibility

  4. Future scalability: Closed ecosystem vs. open data standards

  5. Existing infrastructure: Compatible with current systems vs. need for broader integration

ODC Core:

  • Core framework for managing and analyzing Earth Observation data

  • Focuses on data indexing, storage, and analysis capabilities

  • Uses its own internal data model and indexing system

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Best For:

Users with well-defined, local datasets

Advantages:

Full control over data indexing and storage

Use when:

You need maximum performance for specific, known datasets

ODC STAC:

  • Extension that integrates the SpatioTemporal Asset Catalog (STAC) specification with ODC

  • Allows ODC to work with STAC-compliant data catalogs

  • Simplifies data discovery and access across multiple data providers

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Best For:

Users working with diverse, distributed data sources

Advantages:

Enhanced interoperability and easier data discovery

Use when:

You need flexibility to work with multiple external data catalogs

COMMUNITY RESOURCES

Github

ODC Core Code

Read the Docs 

ODC Documentation

Medium

Follow our blog on Medium

Videos

Open Data Cube videos

GIS Stack Exchange

Developers Forum

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Follow @opendatacube on X

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