It is estimated that England requires up to 345,000 new homes to be built each year in order to keep up with population growth. Although housebuilding has increased since reaching a low point after the nancial crisis, the annual net supply of new homes would need to increase by more than 40% to reach this number. This lack of new homes contributes to higher mortgages, higher rents, less social housing and wider deprivation.
One of the contributing factors is that the current planning application system remains a complex and inefficient task. Every construction project in the UK, including building or extending a house, fitting new windows on a listed building, or chopping down a tree, requires the submission of complicated planning forms and technical drawings. Each one of these documents needs to be manually validated and approved by a planning officer.
Over 3.5 million applications are submitted to councils each year. On average, owing to local government budget cuts of 40% over the last 10 years, it takes three weeks for a council to start looking at a planning application. This creates large backlogs and a lack of information for application submitters, leading to additional calls and emails to chase progress which further increase workload on planning officers. At the same time, over a third (1.2 million) of the applications submitted annually are rejected, often owing to the complexity of submitting a correct application and the manual burden in processing them at councils.
The overall objective of this work is to move towards the automated detection of common errors in planning applications using ML/AI approaches.
Adriaan Hilbers, Imperial College London
Alexandros Bertzouanis, Wilkinson Eyre
Anna Hadjitofi, The Alan Turing Institute
Anthony Peake, Agile Datum
Flora Roumpani, The Alan Turing Institute
Jack Roberts, The Alan Turing Institute
Piers Taylor, Agile Datum
Zachary Price, Agile Datum