Projects:2018s1-108 Machine Learning Multi-Spectral Simulation

2018-10-14T02:00:45Z

A1646280: /* Introduction */

== Introduction ==
This project aims to develop a machine learning system for generating simulated real-world environments from satellite imagery. The environments generated will be incorporated into VIRSuite, a multi-spectral real-time scene generator. The system will need to generate material properties to allow VIRSuite to render the environment realistically from visible to long-wave infrared spectra. The system will need to be able to regenerate existing environments as new satellite images become available, but doesn't need to be real-time or closed loop. Secondary phases of the project are to build a validation process to check that generated environments match real-world likeness across all spectra. The satellite imagery available is up to 0.3m per pixel and the environments generated should reflect this. This project requires students to: gain knowledge in deep and convolutional machine learning technologies, develop and adapt machine learning techniques for satellite imagery (GIS Data), and conduct validation experiments using multi-spectral knowledge and real-life data.

== Project Members ==

=== Supervisors ===
* [[Danny Gibbins]]
* [[Sebastien Wong]]

=== Students ===
* [[Alwin Kai En Ng]]
* [[Thomas Focareta]]

Projects - User contributions [en]

Projects:2018s1-108 Machine Learning Multi-Spectral Simulation