Machine learning for geospatial intelligence

Many diverse geospatial datasets have been used to address significant issues in Great Britain and internationally, such as mapping land use changes, estimating greenhouse gas emissions, planning new development and energy installations, and tracking ecological processes. This project will develop new machine learning (ML) tools to enable effective use of imagery alongside other data types. These tools will enable better decision-making on important societal goals around land use, environmental stewardship and renewable energy deployment. It will directly influence government policy delivery. The project partner (Ordnance Survey) is also interested in how machine learning can be used to make this process faster, or more accurate, or to add new details to the maps. Possible solutions to the problem might involve data compression, feature selection, linking to alternative datasets, or improving the efficiency of training. The student will develop advanced knowledge of image processing, neural networks, high performance computation with GPU arrays, data handling and geospatial techniques. Ultimately, the success of the project will be determined by application of the tools to real-world challenges faced by Ordnance Survey in its role advising the UK government and other clients.