Difference between revisions of "Projects:2018s1-108 Machine Learning Multi-Spectral Simulation"
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== 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 == | == Project Members == | ||
Revision as of 11:30, 14 October 2018
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.
