Machine Learning and Indigenous Perspectives for Reducing Light Pollution

In collaboration with Omexom and the ACT Government, we are working on reducing light pollution in Canberra. This work involves aspects of using machine learning for monitoring, modelling population flows for automation, and looking at impacts on the environment as well cultural impacts.

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This project is open for Summer Scholar, Bachelor, Honours, Master, MPhil and PhD students.
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About

Light pollution does not just affect astronomical observations, but has impacts on native animals as well as impacts cultural connections to the sky.

In collaboration with Omexom and the ACT Government, we are working on reducing light pollution in Canberra.  This work involves aspects of using machine learning for monitoring, modelling population flows for automation, and looking at impacts on the environment as well cultural impacts. We have already shown how adaptive lighting can have a dramatic impact on light pollution.

Projects can span computers science and machine learning, engineering, astronomical observations, social science impacts, and impacts on health. 

Students can apply machine learning models for adaptive lighting -  changing the lighting relative to population activity.  This work is done in collaboration with Omexom and industry/government partners.

 Work on cameras to measure light pollution change relative to dimming, as well as correlations with environmental impacts and/or health impacts.  Students can also look at impacts on features in the sky important to Aboriginal and Torres Strait Islanders.

Members

Supervisor

Indigenous Research Associate
MPhil Student

Astrophysicist and Cosmologist
Research School of Astronomy and Astrophysics