Streamline your workflow

Inspection drones are revolutionary because they can collect mountains of images of your building’s roof or facade. The downside is that someone, somewhere, still has to sift through them to identify defects.

Sometimes, the task falls to a building manager (which can be risky) and sometimes it’s a third-party engineer (which can be costly).

If the company has multiple sites and multiple buildings then the results are more likely to be inconsistent, because so many people will be involved in assessing the imagery.

Skand levels the playing field with an efficient, consistent and scalable form of image analysis that can reduce risk, reduce cost and streamline your workflow. Here’s how.

We create a 'Digital Twin' of the asset

Large roofs with uniform features can make placing defects from images incredibly difficult. This is also the case with placing a defect on a facade where windows and cladding can look exactly the same. Skand uses captured imagery to create a 3D model of the building so users can quickly navigate and understand where defects sit on the building envelope.

Digital Twin

Apply the Skand AI defect engine

Skand deploys Machine Learning (Artificial Intelligence) to learn about defects in imagery. The more images we use to train our AI, the better the model becomes at predicting the likelihood of a defect. Skand has built its AI model with hundreds of thousands of images, and it’s learning more everyday.

Apply Ai

Review by experienced Inspection Staff

Having run the data through Skand's AI defect engine the results of the analysis are passed to our Inspection staff. Our inspectors are trained to identify errors with the AI model and inconsistencies in the defects identified, constantly providing a 'feedback loop' that improves the AI engine with each image analysed.

Review

Finally, Skand deploys a 3-step Quality Control system that comprises of the following:

1 - inspectors exchange work to cross-check each other's sites for missed defects and the categorisation of the metadata inserted into each defect. 

2 - the results of the Skan are passed onto a trained Quality Control Inspector for further analysis. 

3 - the final analysis is handed over to a trained engineer or Thermographer for sign-off.



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