Scalable and efficient geospatial processing in Python.

The mapchete ecosystem provides tools and libraries to process massive amounts of geospatial data. By breaking down large datasets into smaller, manageable tiles, mapchete allows you to run complex algorithms on commodity hardware without running into memory limitations. The processing is parallelized out of the box, ensuring fast and efficient workflows.

Our tools are designed to be modular, user-friendly, and highly extensible, making them suitable for both interactive analysis and large-scale operational pipelines.

The core library and command-line tool. It provides the foundation for tile-based processing of raster and vector data.

• Process Large Datasets: Work with massive raster and vector data without memory issues.
• Parallel Processing: Automatically run computations on multiple CPU cores.
• Simple Configuration: Use easy-to-read `.mapchete files to separate your processing logic from your data configuration.
• Pythonic API: Use mapchete directly from the command line or as a library in your own Python applications.
• Interactive Inspection: Instantly visualize your results on a browser map with the built-in serve command.
• Cloud-Optimized: Designed to work with Cloud-Optimized GeoTIFFs (COGs) and other cloud-native formats.

An extension for Earth Observation (EO) applications. It provides drivers and tools to work with cloud-based EO data sources like SpatioTemporal Asset Catalogs (STAC).

• STAC Integration: Directly process data from STAC Items or STAC API search results.
• Common EO Workflows: Simplifies tasks like creating mosaics and time-series analysis.
• Advanced Sentinel-2 Handling: Built in capabilities like BRDF correction, and reading from various data archives.

A service to discover, manage, and execute mapchete processes via a REST API. It allows you to build web services and workflows around your mapchete processes.

• REST API: Expose your mapchete processes as web services using an OGC API Processes-like interface.
• Job Management: Asynchronously execute and monitor long-running processing jobs.
• Scalable Architecture: Designed to be deployed in containerized environments like Docker and Kubernetes.

There is also a docker image available, based on GDAL and containing the latest releases of mapchete, mapchete EO and mapchete Hub:

  docker pull ghcr.io/mapchete/mapchete:latest

Get a tagged release:

  docker pull ghcr.io/mapchete/mapchete:<release>

mapchete is an open-source project driven by its community. We welcome contributions of all kinds, from bug reports and feature ideas to documentation improvements and pull requests. Check out our CONTRIBUTING.md guide in the main repository to get started.

The mapchete ecosystem is developed and maintained as an open-source community effort. The initial development and many of the core features were made possible with the resources and support of EOX IT Services GmbH.