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
Geography
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
