Road Asset Management helps to maintain and improve existing road networks and is an integral part of keeping the public safe. Through the application of AI, authorities can now apply treatments more effectively, with fewer resources, in a timelier manner. 


Road damage happens everywhere, such as road surface cracks and missing lane markings, which impacts traffic efficiency and sometimes even leads to dangerous situations. Every year, road authorities spend plenty of resources to detect road damage. However, due to human error that arises from manual work, results are not always optimal. To address this concern, a growing number of organizations use computer vision technology to improve detection quality and efficiency. By applying AI algorithm to field-collected videos and images, the road defects can be efficiently detected and classified. This makes the inspection process automated and provides evidence of predictive maintenance to road authorities.


The deterioration of road infrastructures such as traffic signs and guard rails have a large impact on traffic safety and are common factors that lead to road accidents. Therefore, it is important to detect the defects in such important road infrastructure and keep them in good condition. Bypassing traditional methods, Computer Vision, Deep Learning, and GIS techniques will speed up the inventory and maintenance process and reduce the cost of manual labor.


Trees along roads provide a multitude of aesthetic and environmental benefits to citizens. However, when the trees are not in a good condition, they will pose potential risks to vehicles and other road uses. For instance, the overgrown branches may fall in the next storm or grow public infrastructures. Using the traditional approach to build a tree inventory is time-consuming and expensive. Computer Vision and Deep Learning enable fast and effective image and video processing on a large scale, which helps governments and companies efficiently build a comprehensive tree inventory to better support urban forestry management.

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