GeoPython 2020

Creating open collaboration in the cloud with ESA's BIOMASS Multi-Mission Analysis and Algorithm Platform (MAAP)
2020-09-21, 15:50–16:20, Room 1

The goal of the Multi-Mission Algorithm and Analysis Platform of the European Space Agency (ESA-MAAP) is to bring together mission data with hosted processing and collaborative tools. In this talk I will show how we use the Open Source Scientific Python stack to design the platform and create the opportunity to build a community of users for new Earth Observation missions like ESA's BIOMASS mission.

At ESA, we believe that Earth Observation (EO) data and processing driven by novel technological developments has the power to bring the benefit of scientific discovery to everyone. ESA's seventh Earth Explorer mission, the BIOMASS mission, will for example provide crucial information about the state of our forests, how they are changing and the role they play in the global carbon cycle. This mission is designed to provide, for the first time from space, P-band Synthetic Aperture Radar measurements to determine the amount of biomass and carbon stored in forests.
Increasingly, the scientific community faces the need for data storage, computing resources, specialized processing tools, and platforms for knowledge exchange in driving scientific discovery through analyzing this global dataset. Novel and widely-used data processing procedures and tools are needed to develop rapidly, as the BIOMASS mission sensor is the first of its kind. Openly sharing new tools in combination with computing resources allows to include the scientific user community early and has the potential to accelerate the development of new EO data products and foster scientific research conducted by EO data users. The goal of MAAP is to establish an open collaborative framework that allows the scientific community to access and share data, scientific algorithms and computing resources for open EO science.
In this talk, I will introduce how we used the Python ecosystem to create a virtual, open and collaborative environment as a bridge between EO data storage, computing resources and open source algorithm development. I will show how we enable researchers to easily discover, process, visualize and analyze large volumes of data in a version-controlled algorithm development environment. Finally, I will address potential challenges related to collaborative algorithm development, sharing of data and algorithms and how to address them.

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