ASTROSENSE: applying astrophysics algorithms to remote sensing data

Machine learning is a computational data analysis technique that offers tremendous benefits over traditional methods. In particular, algorithms can be developed that can automatically identify objects or features of interest in digital imaging. Some machine learning algorithms require extensive training through labelled examples, but unsupervised algorithms can learn from the data itself, requiring no pre-labelled training set. This makes such algorithms incredibly versatile and can be easily applied to many different types of imaging. In principle, the algorithm's performance should improve over time as it 'experiences' more examples of input data. We are developing just an unsupervised machine learning algorithm for use in large-scale astronomical surveys that can also be applied in other 'remote sensing' data, such as underwater sonar imaging of the sea bed and aerial/satellite imagery. Such an algorithm can, for example, help determine the local terrain and identify hazards in complex, changing environments that could be missed by a human inspector. This could feed into AI-assisted navigation units in autonomous vehicles for example. Our goal in this project is to develop a versatile, robust algorithm that can be deployed in a variety of practical areas, with a view to performing real-time image classification and analysis on input data, both from astrophysics and 'real-world' industrial sectors.