Research topics at OS

3D and height

We're working to anticipate our customers' 3D needs – from managing assets to planning property developments and assessing areas of land at risk.
3D and height data

Remote sensing platforms

As the detail and imagery resolution in our products increases, it becomes even more vital that we find new ways to source accurate data.
Alternative remote sensing platforms

Automatic change detection

We need to know when new houses have been built and sites have been redeveloped. We get these updates from many sources.
Keeping our mapping up to date

Cartographic generalisation

We continuously look for ways to improve our map data and its legibility. Some roads may conflict, for example, when the map is at a small scale.
Improving map information

Crowd sourcing

We research all aspects of crowd sourcing from technologies and techniques to evaluating the potential and quality of data from the crowd.
Crowd sourcing data

Collecting data

We look at how we can make our data even better, by improving our capture processes and by using novel techniques and sources to get new data.
Data acquisition

Enriching place-based information

Place names are key to anyone using a map or map data. We look at how we can achieve more information-rich content specific to user activities.
Enriching information

Data science

We explore new ways of both enriching data which includes focusing on analytics and knowledge discovery, and generalising data so that it can be represented in diverse ways.
Delving into data science


Location is in everything and is at the core of our research. We're interested in how people interact with geography in leisure, business and the public sector.
Exploring geography

Graph databases

As our data evolves, we need a flexible infrastructure that will do the same so that we can store our information efficiently and manage links between core data and third party data.
Handling our data

The linked data web

Location is everywhere and with so much mapping data it's key that we connect data sets and link information together through one single point.
Linking data together

Machine learning

We must learn to make assumptions about 'invisible' data based on the real data that we collect, to find patterns and help predict future changes.
Predicting the future

Assembling linked data applications

RAGLD (The Rapid Assembly of Geo-Centred Linked Data Applications) was a collaborative research initiative which focused on harnessing large sources of data.
RAGLD initiative

Technical infrastructure

It's important to have the right systems to deal with all of our data. We research how best to store and maintain our data and how to deliver information in new ways.
Managing and delivering data

User needs and usability

We're interested in how people interact with location information on a daily basis and how we can improve the design and development of our data.
Using location information