Not a NASA moon mission from the distant future. Not a Victorian revival of Ancient Greek religion. Just a group of people trying to run code in space!
We're a group of teams entering the Astro Pi: Mission Space Lab competition run by the Raspberry Pi Foundation and the European Space Agency.
- The competition is likely to start again in November 2024.
Click on the team name to go to their main repository. Other repositories connected to that team are listed in brackets.
This year in Astro Pi all teams needed to measure the speed of the ISS. Our code was private during development but the available online resources pushed us all towards a Computer Vision approach.
These repositories are currently private because the task is exactly the same for the current 2024-25 season, and we want to prevent cheating.
- 🪨 Asteroid
- 🤭 Oopsies
- 🌅 Solaris
- 🌃 Stardust (did not finish code in time)
This year Astro Pi was more creative, and the format allowed all teams to come up with their own experiment ideas. 📰 The reports show the range of results we got!
- 🌇 Asteria (Post processing) (📰 Report) - Calculating plant health (NDVI) on Earth, comparing it to GDP, and looking at the effects of urbanisation on it.
- ⛈️ Chululof - Examining the link between NDVI and storms. (did not finish code in time)
- 🪙 Ganymede (📰 Report) - Comparing NDVI plant health, environmental damage, and country GDP.
- 🚫 hastyreg - Unplanned. (did not finish code in time)
- 🪴 Plankton (Post processing) (📰 Report) - Analysing the effects of urbanisation on the Earth's plant health.
This year Astro Pi was more creative, and the format allowed all teams to come up with their own experiment ideas. 📰 The reports show the range of results we got!
- 🎈 Team B (Post processing) (📰 Report) - Determining the concentration of greenhouse gases using NDVI in different parts of the earth’s atmosphere and mapping the earth’s magnetic field to determine how its strength and shape changes according to topographical features.
- 🌳 knowNDVI (Post processing) (📰 Report) - Calculating plant health (NDVI) on Earth and comparing it to factors from NASA datasets and datasets we create, like air pollution, population density, temperature, humidity, daylight time, cloud cover, longitude and latitude, to get an idea of how limiting factors work on a large scale.
- ☁ Cloud9 - Investigating how location, altitude, land type and plant health affect cloud distribution. (did not reach phase 3 - code ran with error)
