Moon Exploration for All of Us

Do you want to join the challenge to explore the moon? https://phys.org/news/2022-09-explore-moon.html 

 

 

There is a challenge (competition with EUR1,500 or THB54,000 prize) in building (models) of Machine Learning (ML) tools for use on exploration of an area on the Moon.

--The Machine Learning Lunar Data Challenge is in three steps: firstly, participants should train and test a model capable of recognizing craters and boulders on the lunar surface. Secondly, they should use their model to label craters and boulders in a set of images of the Archytas zone. Finally, they should use the outputs of their models to create a map of an optimal traverse across the lunar surface to visit defined sites of scientific interest and avoid hazards, such as heavily cratered zones. 

--The public and schools are also invited to use lunar images to identify features and plot a journey for a rover. Prizes for the challenges include vouchers totaling 1500 Euros, as well as pieces of real moon rock from lunar meteorites.

There is a lot of information on machine learning on the Net. We can start with Wikipedia https://en.wikipedia.org/wiki/Machine_learning ,   What is machine learning?  https://www.ibm.com/au-en/cloud/learn/machine-learning  and learn to use some tools on Machine Learning Crash Course
with TensorFlow APIs https://developers.google.com/machine-learning/crash-course . 

THere are also  many ML projects on Github ( ).  Machine Learning for Beginners - A Curriculum https://github.com/microsoft/ML-For-Beginners is worth a look. A survey of machine-learning-projects https://github.com/topics/machine-learning-projects can give more ideas and code examples (ready to modify and use).

This challenge is surely better than the doldrums among the COVID-19, Ukraine War, Floods and Politicking in Thailand. ;-)