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Machine Learning and AI in the Built Environment - Dr Thies Lindenthal

This project improved the foundations for applying tried-and-tested machine learning (ML) approaches to the built environment. This mini project reduced the cost of creating and deploying ML systems by creating versatile and extendable API’s, data management infrastructure and mobile apps. A future version of the API’s might be commercialised in areas like mortgage origination, insurance claim processing or property tax (non-UK, though) estimation.

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This project improved the foundations for applying tried-and-tested machine learning (ML) approaches to the built environment. If anything, the cost of developing ML systems has dramatically fallen in the last years – and is expected to further decrease in the future. Still, collecting data for training and evaluation of machine learning system remains complex and costly. Deploying a trained model for inference at a large scale, soliciting feedback on model predictions and managing the flow of expert feedback into new iterations of a model is a also challenge.

This mini project reduced the cost of creating and deploying ML systems by creating versatile and extendable API’s, data management infrastructure and mobile apps. A future version of the API’s might be commercialised in areas like mortgage origination, insurance claim processing or property tax (non-UK, though) estimation.

How can we cost-efficiently scale ML research in the built environment from a one-city scope to the national level, manage training and evaluation data, deploy production systems for inferences and collect feedback from human experts efficiently? We believe that new software tools are pivotal in addressing three core challenges for researchers utilising ML in the built environment.

1. Collect expert input and feedback efficiently

2. Integrate with other software/apps

3. Store and display data, models and estimates

The output of this project already facilitated new academic research in urban economics and real estate. For instance, the automatic classification of the UK housing stock into vintages is underway and its coverage can be followed in real time at this interactive map: https://thies.carto.com/builder/359c34d0-9d3b-481a-b3b9-abb75bced8e6/embed

Researchers:


Land Economy, Department of Economics


University of Alabama
Department of Economics

 

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Welcome to the Centre for Digital Built Britain.  

The Centre for Digital Built Britain is a partnership between the Department of Business, Energy & Industrial Strategy and the University of Cambridge to deliver a smart digital economy for infrastructure and construction for the future and transform the UK construction industry’s approach to the way we plan, build, maintain and use our social and economic infrastructure.