For companies working on roof renovation, asbestos removal and photovoltaic systems, finding the industrial buildings to work on still means site visits and manual research. We developed a system that analyses entire areas from satellite imagery and returns a list of roofs that are already measured, classified and linked to the businesses occupying each building.
A computer vision pipeline detects building outlines by matching the map with the satellite image, splits them into roof sections, calculates area and perimeter in metres from geographic coordinates and crops each roof. Each roof is then linked to the nearest businesses.
Classification relies on transfer learning: a pre-trained convolutional neural network extracts the features of each image and four dedicated classifiers recognise the structure (flat, shed, vault, gable…), the material (membrane, metal, fibre cement, asbestos…), the presence of photovoltaic panels and whether the roof is new. The classifiers are trained on roofs catalogued by the client’s technicians with a purpose-built web tool.
Models are trained and run on the company’s own servers, without sending images to external AI services.