OS Machine Learning Metrics
Lead Participant:
ORDNANCE SURVEY LTD
Abstract
Ordnance Survey's research and development team routinely uses machine learning to extract new information from existing data sources. As machine learning is a relatively new field, there is a need to provide high-quality metrics to help understand the data quality of machine learning outputs.
This project will create a new range of tools and processes to describe and quantify the quality of Ordnance Survey's machine learning outputs. These tools and processes will use different testing methodologies as well as comparative assessments of networks to create benchmarks for accuracy. The project will establish a regulated quality control metric for Ordnance Survey's machine learning models to ensure its processes stand up to the growing accuracy requirements demanded by its widespread customer base.
This project will create a new range of tools and processes to describe and quantify the quality of Ordnance Survey's machine learning outputs. These tools and processes will use different testing methodologies as well as comparative assessments of networks to create benchmarks for accuracy. The project will establish a regulated quality control metric for Ordnance Survey's machine learning models to ensure its processes stand up to the growing accuracy requirements demanded by its widespread customer base.
Lead Participant | Project Cost | Grant Offer |
---|---|---|
ORDNANCE SURVEY LTD | £52,763 | £ 26,382 |
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Participant |
||
THE SCIENCE AND TECHNOLOGY FACILITIES COUNCIL | £99,972 | |
ORDNANCE SURVEY LIMITED | ||
NPL MANAGEMENT LIMITED | £85,458 | |
SCIENCE AND TECHNOLOGY FACILITIES COUNCIL | ||
NPL MANAGEMENT LIMITED |
People |
ORCID iD |
Paul Cruddace (Project Manager) |