Automated Low-Cost Change Detection of Road Infrastructure Assets Using Remote Sensing and AI

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Highway infrastructure across Canada is a key national asset that is critical to the mobility and economic prosperity of Canadians. Unfortunately, maintaining highway infrastructure is a costly and resource draining endeavor. Provincial transportation agencies dedicate billions of dollars to the process on an annual basis. In current practice inspecting the conditions of highway infrastructure assets is a tedious process whereby a crew of at least two individuals drive thousands of kilometers of road at low speeds to identify assets' where the conditions have changed since the initial installation. For primary highways the same highway is driven every day and crew stop by at assets in search for any changes in the condition. To help automate the process and improve its efficiency and accuracy, this project will involve developing a low-cost framework that uses remote sensing technology to create virtual 3D models of infrastructure assets. Algorithms that employ principles of machine learning and Artificial Intelligence will then be developed to process the 3D models and provide inspection crew with information about the changes in asset conditions without the need to visit each asset during long and tedious site visits. The results will be shared with the project partner who will work on large scale deployment of the developed technology to improve highway maintenance practice across Canada. This will cause significant improvements in the way infrastructure asset inspections are conducted resulting in safer and more resilient roads at a significantly lower cost to tax-payers.