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Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

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Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production Year2022

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The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (maap-project.org) and ESA (esa-maap.org/) landing pages.
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Mathematical analysisAlgorithmProduct (business)Texture mappingCollaborationismProcess (computing)AreaProduct (business)Level (video gaming)Projective planeResultantEstimatorVarianceHarmonic analysisComputer animation
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Transcript: English(auto-generated)
Hi, I'm Stinger Gualla from NASA. I'll just take a couple minutes to introduce you to the multi-mission Algorithm and analysis platform or map as we call it It's our premier, you know, it's really a flagship Project to show international collaboration and also for open science and the whole thing is open source
And this is a substantial undertaking. There are about 20 people working on this And it's a fully collaborative thing between the European Space Agency and NASA and so why did we do this?
Well, the original use case is understanding the carbon cycle, which is the weight of all trees on earth Basically, your weight doesn't matter. It's the trees that are are big here And we have we have a lot of data on that. There's
Early next year Nisar is going to launch and so is biomass and those are going to bring in huge amounts of data It's not trivial Nisar does about is going to do about 80 terabytes a day of data So we need a collaborative environment. We need to get the processing next to the data. We need to be able to
Program together and share algorithms stuff like that. So the solution map It's a joint coding environment you log in you get your environment all your libraries are updated. How cool is that? You have a chat and collaborative coding environment. We have a common metadata repository so that
Everyone addresses the data the same way. It's all quite it's all in the cloud And it all uses common api's Everything in it is standardized and common So everyone wherever you're sitting Europe us anywhere it works
We have our own individual things that we're building out on the sides of map Europe has this container organization orchestration pipeline copa, which is kind of cool and They have a really good visualization platform eDAV
On the NASA side we've got the data processing system where you you can spin up a supercomputer in the cloud if you can afford it but we also So we can afford it have a pipeline to an actual supercomputer Pleiades and we also have the map dashboard so that we can communicate
the results out of map directly Users can do that. So some products are coming out of map We're doing a huge biomass harmonization project. So everyone who's ever done a large biomass project We get them on map and we're harmonizing it so that you know, all the variances
Is accounted for stuff like that And then we have I sat two we did boreal biomass. So Jedi only goes up to like London and We made it compatible so we can put Jedi and I sat two together and have another global biomass
Estimate and we've done all the preliminary work for nice are and if you want to know more about map I would start with a press release up here at the top and then our github is at the bottom and maps already being Reused this is not a show off github. This is actual usable code Natasha Stavros at the University of Colorado
Boulder for example has already implemented her own map and made a much prettier front end to it for hyperspectral imagery And you are all welcome to do that. Thank you