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

加拿大的公路基础设施是一项重要的国家资产，对加拿大人的交通和经济繁荣至关重要。不幸的是，维护高速公路基础设施是一项昂贵且消耗资源的工作。省级交通运输机构每年在这一过程中投入数十亿美元。在目前的实践中，检查公路基础设施资产的状况是一个繁琐的过程，至少两个工作人员以低速驾驶数千公里的道路，以确定自初始安装以来条件发生变化的资产。对于主要高速公路来说，同样的高速公路每天都在行驶，工作人员会在资产处停下来寻找条件的任何变化。为了帮助自动化流程并提高其效率和准确性，该项目将涉及开发一种低成本框架，该框架使用遥感技术创建基础设施资产的虚拟3D模型。然后将开发采用机器学习和人工智能原理的算法来处理3D模型，并为检查人员提供有关资产条件变化的信息，而无需在漫长而繁琐的现场访问期间访问每个资产。研究结果将与项目合作伙伴分享，该合作伙伴将致力于大规模部署所开发的技术，以改善加拿大各地的公路维护实践。这将大大改善基础设施资产检查的方式，从而使道路更安全、更有弹性，同时大大降低纳税人的成本。

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.