Ordnance Survey (OS) manage many diverse geospatial datasets 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. Data held includes huge amounts of aerial photography and satellite imagery, alongside other spatial land use data such as buildings, transport networks, ecological features, hydrology and topology. 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.
Experts at OS currently use the imagery datasets as part of a largely manual process to update their land use maps. OS is greatly interested in how machine learning can be used to make this process faster, or more accurate, or to add new details to the maps, for exampl, details of roof shapes or street furniture such as lampposts and traffic lights.
The imagery datasets held are huge. For example, regular aerial photography provides imagery of the whole of Great Britain at 25cm grid resolution. This presents a major challenge to the development of machine learning tools, in that the dataset is too large for efficient processing. For example, it can take up to three weeks of high-performance computation to train a single ML model for land use classification.
The aim of this project is to explore techniques for data sampling and pre-processing that will improve performance by retaining important information and reducing information redundancy. The student will work with world-leading researchers at the University of Exeter and Ordnance Survey to generate simplified datasets, train machine learning models, and establish reliable and efficient pipelines for data processing. 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. Thus the student will gain broad experience of the end-to-end deployment of advanced ML and geospatial analysis.
The student will be based within the Centre for Doctoral Training in Environmental Intelligence at the University of Exeter, within an interdisciplinary cohort of postgraduate researchers studying diverse topics in environmental data science. They will also spend time on placement with the Ordnance Survey research team during the project. After completion of the PhD thesis, the student will be employed by Ordnance Survey on a 12-week postdoctoral contract to facilitate knowledge exchange and implementation.
The studentship is funded via the EPSRC Industrial Cooperative Awards in Science & Technology (iCASE) scheme, via a grant awarded to Ordnance Survey. For more details about the Industrial CASE scheme, see https://epsrc.ukri.org/skills/students/industrial-case/intro/
If you have questions or for more information, please email Dr Chunbo Luo ([Email Address Removed]).
About the UKRI Centre for Doctoral Training in Environmental Intelligence:
Our changing environment presents many challenges that will affect everyone’s future health, safety, and prosperity. Environmental Intelligence (EI) is the integration of environmental and sustainability research with data science, artificial intelligence, and cutting-edge digital technologies. EI research aims to address 21st century environmental challenges and mitigate the societal impacts of environmental change.
The CDT in Environmental Intelligence provides an interdisciplinary training programme for students covering a range of skills in data science and AI, environmental science, and responsible innovation. The CDT cohort (around 40 students) works and learns together, bringing diverse knowledge, skills, and interests from a range of academic disciplines. As part of the research community at the University of Exeter, students benefit from wider networking with colleagues in the Institute for Data Science and Artificial Intelligence: the Global Systems Institute; and the Environment and Sustainability Institute.
The student will be based at the University of Exeter in the UK and will be expected to work primarily on Streatham campus in Exeter. There may be an opportunity to spend time on placement at the Ordnance Survey offices in Southampton.
This project will suit a student with a good undergraduate (BSc) degree in a strongly numerate discipline (e.g. data science, computer science, mathematics, physics, statistics). A postgraduate (MSc) qualification in a relevant discipline is desirable but not essential. Related knowledge and experience in image processing will be particularly advantageous. Candidates unsure of their suitability are encouraged to enquire informally before applying (contact Dr Chunbo Luo, [Email Address Removed]).
The CASE funding provides an enhanced stipend and tuition fees for a UK student. International students are welcome to apply but will need to cover the additional tuition fees liable to International students at University of Exeter (more information here.
English language requirements - (Profile A) at https://www.exeter.ac.uk/pg-research/apply/english/
How to apply
In the application process you will be asked to upload several documents:
· Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
· Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying).
· Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to [Email Address Removed] quoting the studentship reference number - 4647
· If you are not a national of a majority English-speaking country, you will need to submit evidence of your proficiency in English.
This studentship will remain open for applications until a suitable candidate is recruited.
Applications will be considered as soon as they are received.
The final closing date for this call will be midnight Monday 2nd January 2023, but the call will close sooner if a suitable candidate is recruited.