The MAAP platform is designed to combine data, algorithms, and computational abilities for the processing and sharing of data related to NASA’s GEDI, ESA’s BIOMASS, and NASA/ISRO’s NISAR missions. These missions generate vastly greater amounts of data than previous Earth observation missions. There are unique challenges to processing, storing, and sharing the relevant data due to the high data volume as well as with the data being collected from varied satellites, aircraft, and ground stations with different resolutions, coverages, and processing levels.
MAAP aims to address unique challenges by making it easier to discover and use biomass relevant data, integrating the data for comparison, analysis, evaluation, and generation. An algorithm development environment (ADE) is used to create repeatable and sharable science tools for the research community. The software is open source and adheres to ESA’s and NASA’s commitment to open data.
NASA and ESA are collaborating to further the interoperability of biomass relevant data and metadata. Tools have been developed to support a new approach to data stewardship and there is a data publication workflow for organizing and storing data and generating metadata to be discoverable in a cloud-based centralized location. The platform and data stewardship approaches are designed to ease barriers and promote collaboration between researchers, providers, curators, and experts across NASA and ESA.
An overview of the MAAP platform
- The Algorithm Development Environment (ADE) is a tool that helps with the development of algorithms in a consistent, standardized environment that helps with the development and testing of algorithms and facilitates large scale data processing. MAAP’s primary user interface is Jupyterlab, where code is written and tested before pushed to the large scale data processing system. Code is stored and checked out from Git-based repositories, including Github and MAAP’s own code repository subsystem.
- The Data Processing System (DPS) is where registered algorithms (see Algorithm Catalog) can be run at scale in the cloud. The MAAP system provides a Jupyter GUI to run Jobs, or the maap.py library can be used to run a batch of Jobs in a loop using Python. The DPS also has monitoring capabilities, and again the MAAP system provides a Jupyter GUI to help monitor Jobs. This can also be done using maap.py in Python.
- The Algorithm Catalog, where your algorithms from the ADE can be registered and compiled for use by the DPS. The MAAP system provides API and GUI tools to help you register and view your algorithms.
- The Code Repository is a git-based repository to store user code. It is also used to store the configuration files necessary for building algorithms to store in the algorithm catalog and for execution in the DPS.
- Input data comes from a few Data Catalogs. Currently there is a MAAP STAC Catalog and the NASA CMR Catalog. More information can be found in the search tutorials section on our documentation site.