Amazing science from the winners of Astro Pi Mission Space Lab 2019–20

Amazing science from the winners of Astro Pi Mission Space Lab 2019–20

The Astro Pi jury has chosen ten winning and eight highly commended Mission Space Lab teams whose science experiments were truly impressive.

The team at Raspberry Pi and our partner ESA Education are pleased to announce the winning and highly commended Mission Space Lab teams of the 2019–20 European Astro Pi Challenge!

Astro Pi Mission Space Lab logo

Mission Space Lab sees teams of young people across Europe design, create, and deploy experiments running on Astro Pi computers aboard the International Space Station. Their final task: analysing the experiments’ results and sending us scientific reports highlighting their methods, results, and conclusions.

One of the Astro Pi computers aboard the International Space StationOne of the Astro Pi computers aboard the International Space Station

The science teams performed was truly impressive, and the reports teams sent us were of outstanding quality. A special round of applause to the teams for making the effort to coordinate writing their reports socially distant!

The Astro Pi jury has now selected the ten winning teams, as well as eight highly commended teams:

And our winners are…

Vidhya’s code from the UK aimed to answer the question of how a compass works on the ISS, using the Astro Pi computer’s magnetometer and data from the World Magnetic Model (WMM).

Unknown from Externato Cooperativo da Benedita, Portugal, aptly investigated whether influenza is transmissible on a spacecraft such as the ISS, using the Astro Pi hardware alongside a deep literature review.

Space Wombats from Institut d’Altafulla, Spain, used normalized difference vegetation index (NDVI) analysis to identify burn scars from forest fires. They even managed to get results over Chernobyl!

Liberté from Catmose College, UK, set out to prove the Coriolis Effect by using Sobel filtering methods to identify the movement and direction of clouds.

Pardubice Pi from SPŠE a VOŠ Pardubice, Czech Republic, found areas of enormous vegetation loss by performing NDVI analysis on images taken from the Astro Pi and comparing this with historic images of the location.

NDVI conversion image by Pardubice Pi team – Astro Pi Mission Space Lab experimentNDVI conversion image by Pardubice Pi team

_Reforesting Entrepreneurs _from Canterbury School of Gran Canaria, Spain, want to help solve the climate crisis by using NDVI analysis to identify locations where reforestation is possible.

_1G5-Boys _from Lycée Raynouard, France, innovatively conducted spectral analysis using Fast Fourier Transforms to study low-frequency vibrations of the ISS.

Cloud4 from Escola Secundária de Maria, Portugal, masterfully used a simplified static model and Fourier Analysis to detect atmospheric gravity waves (AGWs).

Cloud Wizzards from Primary School no. 48, Poland, scanned the sky to determine what percentage of the seas and oceans are covered by clouds.

Aguere Team 1 from IES Marina Cebrián, Spain, probed the behaviour of the magnetic field, acceleration, and temperature on the ISS by investigating disturbances, variations with latitude, and temporal changes.

Highly commended teams

Creative Coders, from the UK, decided to see how much of the Earth’s water is stored in clouds by analysing the pixels of each image of Earth their experiment collected.

Astro Jaslo from I Liceum Ogólnokształcące króla Stanisława Leszczyńskiego w Jaśle, Poland, used Reimann geometry to determine the angle between light from the sun that is perpendicular to the Astro Pi camera, and the line segment from the ISS to Earth’s centre.

Jesto from S.M.S Arduino I.C.Ivrea1, Italy, used a multitude of the Astro Pi computers’ capabilities to study NDVI, magnetic fields, and aerosol mapping.

BLOOMERS from Tudor Vianu National Highschool of Computer Science, Romania, investigated how algae blooms are affected by eutrophication in polluted areas.

AstroLorenzini from Liceo Statale C. Lorenzini, Italy used Kepler’s third law to determine the eccentricity, apogee, perigee, and mean tangential velocity of the ISS.

Photo of Italy, Calabria and Sicilia by AstroLorenzi team — Astro Pi Mission Space Lab experimentPhoto of Italy, Calabria and Sicilia (notice volcano Etna on the top right-hand corner) captured by the AstroLorenzi team

EasyPeasyCoding Verdala FutureAstronauts from Verdala International School & EasyPeasyCoding, Malta, utilised machine learning to differentiate between cloud types.

BHTeamEL from Branksome Hall, Canada, processed images using Y of YCbCr colour mode data to investigate the relationship between cloud type and luminescence.

Space Kludgers from Technology Club of Thrace, STETH, Greece, identified how atmospheric emissions correlate to population density, as well as using NDVI, ECCAD, and SEDAC to analyse the correlation of vegetation health and abundance with anthropogenic emissions.

astro pi space astro pi mission space lab data science

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