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

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 is a costly and resource draining endeavour. 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 everyday. 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 collect data about infrastructure assets. Algorithms that employ principles of machine learning and Artificial Intelligence will then be developed to process the data 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.

Faculty Supervisor:

Suliman Gargoum

Student:

Partner:

Nektar;Ledcor Highways Ltd.

Discipline:

Engineering

Sector:

Construction and infrastructure; Information and cultural industries

University:

The University of British Columbia - Okanagan

Program: