An overview of the python client.
Create, manage, search, and visualize geospatial data with Catalog.
The Descartes Labs Catalog API serves as a comprehensive repository for geo-referenced data. It offers access to a vast collection of Descartes Labs-provided geospatial data, amounting to approximately 30 Petabytes. Additionally, users can augment the Catalog with their own data products and any derivative data generated on the Platform.
One of the Catalog's key advantages is its ability to simplify access to spatiotemporal raster data. This enables customers to swiftly search for and extract the most pertinent data for their specific needs. Moreover, the Catalog provides a high-throughput data feed and storage mechanism, which, when paired with Descartes Labs-provided compute capabilities, empowers global-scale modeling for the world's most complex problems.
Furthermore, the Catalog offers specialized storage and access to geo-referenced Vector data. It also provides flexible file storage for model weights, results, and other types of data. Each dataset within the Catalog can be independently managed, searched, and shared, making it an advanced collaboration platform for geospatial projects.
Leverage the power of scalable batch computing in Descartes Labs' cloud infrastructure to parallelize your analysis.
When you need to deploy a model across a wide area, the Compute service offers users the ability to utilize cloud our computing infrastructure to parallelize and run code on a large scale. Users' Python code is packaged and executed on nodes that are hosted within Descartes Labs cloud infrastructure. This provides a flexible foundation for running complex machine learning and artificial intelligence algorithms on Catalog data.
Dynamic Compute, our powerful on-demand geospatial analysis engine, quickly develops and prototypes your projects.
Dynamic Compute allows users to focus on their specific problem rather than getting caught up in the details of the data. It provides users with a live-updating interactive map that displays their analysis. Users can combine different operations on Catalog data and add them to the map. These operations are computed as the user explores their area of interest on the map. This interactive approach allows users to develop analyses without needing to know specific geographic coordinates or deal with the complexities of the dataset.
Note: Dynamic Compute is a separate Python package, install via:
pip install descarteslabs-dynamic-compute
API Documentation and Guides
To learn more about the Python APIs, you can refer to the comprehensive API documentation available at docs.descarteslabs.com. Additionally, Descartes Labs provides detailed Example Notebooks available publicly on GitHub.