Automated data collection and machine learning methods for civil infrastructure condition assessment in sparsely inhabited regions of Canada

加拿大是世界上第二大国家，但它是世界上人口密度最低的国家之一。大约三分之二的人口居住在该县南部边境100公里以内，仅占加拿大总领土的4%。尽管人口分布不均，但庞大的民用基础设施网络遍布加拿大人口稀少的地区。这些资产对于土著和当地社区的公平获取以及社会和经济繁荣至关重要，许多农业和工业活动都依赖于其可靠的性能。许多研究工作开发了民用基础设施的诊断和管理方法，但这些项目大多集中在人口稠密地区的主要资产上。例如，道路状况评估领域的大多数研究开发了用于评估铺设道路的方法。然而，在加拿大的偏远地区，未铺设的道路在运输和服务活动中发挥着重要作用，该国约60%的道路网是未铺设的。除了道路，许多重要的基础设施资产，如水利结构，分散在加拿大的偏远地区。这些资产中有许多是几十年前建造的，其中相当一部分被加拿大基础设施报告卡评为公平或低于公平水平。传统的基础设施状况评估方法是劳动密集型和昂贵的，由于缺乏专家和需要覆盖的距离很长，这些问题在偏远地区更成问题。 该研究计划旨在通过应用先进的数据收集和机器学习方法，开发提高偏远地区状况评估和数据分析自动化水平的方法。该研究计划的主要目标可以概括为：1）弥合最先进的计算机视觉和机器学习算法与基础设施状况评估领域的实际应用之间的差距; 2）以数据为中心管理加拿大偏远地区的基础设施资产。拟议的研究计划包括几个研究项目，每个项目将调查该领域的一个特定和相关的主题。该研究计划将有可能将基础设施资产的状况评估从传统的做法转变为具有高自动化水平的数据驱动的做法，从而提高状况评估过程的效率和准确性。此外，制定的方法将促进当地社区对基础设施管理的贡献，并减少外包条件评估工作的必要性。提高偏远地区状况评估的自动化水平将节省成本，并改善加拿大农村地区基础设施的适用性。

Canada is the second largest country by total area, but it has one of the lowest population densities in the world. Approximately two-thirds of the population resides within 100 kilometers of the county's southern border, which accounts for only 4% of Canada's total territory. Despite this uneven distribution of the population, vast networks of civil infrastructure are spread throughout the sparsely inhabited regions of Canada. These assets are critical for equitable access and social and economical prosperity of Indigenous and local communities, and many farming and industrial operations depend on their reliable performance. Many research efforts have developed methods for diagnostics and management of the civil infrastructure, but the majority of these projects focused on major assets that exist in densely populated regions. For example, most of the research in the area of road condition assessment developed methods for assessment of paved roads. Unpaved roads, however, have a major role in transportation and service activities in the remote regions of Canada, where about 60% of the country's road network is unpaved. In addition to the roads, many vital infrastructure assets, such as hydraulic structures, are scattered in the remote regions of Canada. Many of these assets were built decades ago and a considerable portion of them are rated in fair or below fair levels by the Canadian Infrastructure Report Card. Conventional infrastructure condition assessment methods are labour-intensive and expensive, and these issues are more problematic in remote regions due to the shortage of experts and the long distances that need to be covered. This research program aims at developing methods to enhance the automation level of condition assessment in remote regions and data analysis by the application of advanced data collection and machine learning methods. The main objectives of this research plan can be summarized as: 1) bridge the gap between the state-of-the-art computer vision and machine learning algorithms and practical applications in the field of infrastructure condition assessment; 2) data-centric management of the infrastructure assets in the remote regions of Canada. The proposed research program consists of several research projects and each one will investigate a specific and related topic in this area. This research program will potentially transform condition assessment of infrastructure assets from conventional to data-driven practices with high level of automation, leading to improvements in efficiency and accuracy of the condition assessment processes. In addition, the developed methods will facilitate the contribution of local communities to the management of infrastructure and reduce the need for outsourcing condition assessment efforts. The increased automation level of the condition assessments in remote regions will result in cost savings and improvement in the serviceability of the infrastructure in the rural regions of Canada.