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Empowering Global Insights:

Open Source Earth Observation at Scale

The Open Data Cube (ODC) is a free, open-source software package that simplifies the management and analysis of large amounts of satellite imagery and other Earth observation data. It allows users to easily access, process, and analyze decades of geographical data to track changes on Earth's surface over time. ODC is designed to help scientists, researchers, and government agencies make better-informed decisions in areas such as environmental issues, land use, and resource management.

 

Use Case Examples

Land Cover Mapping in Australia: Researchers used the Open Data Cube through Digital Earth Australia (DEA) to generate annual national-scale land cover maps of Australia, leveraging Landsat imagery from 1988-2020. This work implemented the FAO Land Cover Classification System (LCCS) Level 3 to produce six main land cover classes at 25m resolution, supporting environmental monitoring and policy-making.

Citation: Owers, C. J., et al. (2022). "Operational continental-scale land cover mapping of Australia using the Open Data Cube." International Journal of Digital Earth, 15(1), 1715-1737.

Open Source

Flexible Data Handling

As an open-source project, the ODC promotes collaboration and transparency. Users can inspect the code, contribute improvements, and customize the platform to suit their specific needs. This open nature also ensures long-term sustainability and community-driven development.

Interoperability

The ODC simplifies data management by leveraging Analysis Ready Data (ARD), which reduces preprocessing time and effort. It supports a wide array of data formats, including GeoTIFF, Cloud Optimized GeoTIFF (COG), NetCDF, and CSV. This versatility allows users to work with diverse datasets without the need for extensive format conversions.

Integration with other systems is straightforward thanks to the ODC's REST API, which enables easy connection to web services and third-party software.

Custom Algorithms

Researchers and data scientists can develop and implement custom algorithms in Python, tailoring the ODC to their specific analytical needs. For those seeking quick results, the ODC also includes pre-built algorithms for common analyses such as NDVI calculation and change detection. This balance of flexibility and convenience makes the ODC suitable for a wide range of users, from beginners to advanced practitioners.

Scalable Performance

The ODC is designed to handle large-scale data processing efficiently. It can be deployed on high-performance computing clusters and cloud platforms, allowing users to scale their analyses as needed. By leveraging Dask for parallel computing, the ODC can process vast amounts of data quickly, making it suitable for both small projects and large-scale national or global analyses.

Institutional Partners:
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