The AI4EOSC project will deliver an AI-based solution that leverages thermal UAV-based imaging to identify thermal anomalies in urban settings. Whether it's on building rooftops caused by thermal bridges or on the ground caused by urban features, our AI model will automatically identify thermal anomalies with pinpoint accuracy.
The solution uses Deep Learning models to detect hotspots through instance segmentation in combined thermal and RGB image data. The technology will be hosted on a cloud-based automated service that leverages best practices and technology advances of the AI4EOSC platform, potentially using decentralised learning techniques such as federated learning by selecting each client according to the geographic location where an image was taken.
The target audience for this use case includes urban planners, building owners, and district heating network operators who struggle to maintain high energy efficiency due to the inability to quickly and accurately pinpoint the location of heat loss. The goal is to automate the detection of thermographically salient heat losses to accelerate the implementation of necessary countermeasures and repairs to mitigate their effects.
An exciting venture to improve energy efficiency in urban settings through the power of AI and thermography.