Integration of Remote Sensing Big Data into the Management and Design of Highway Infrastructures

遥感大数据融入公路基础设施管理和设计

• 批准号: RGPIN-2019-04576
• 负责人: ElBasyouny, Karim
• 金额: $ 2.26万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715016
• 关键词: Integration; Remote; Sensing; Big; Data

The focus of this Discovery Grant (DG) is to develop methods to process big LiDAR data sets efficiently and accurately with the ultimate goal of helping make infrastructure management and highway design processes much more informed and data-driven. Light Detection and Ranging (LiDAR) technology is one such remote sensing technique that has the potential to transform the surveying process in highway engineering. During a single survey pass, mobile LiDAR equipment can create an accurate three-dimensional representation of highway infrastructure in the form of a highly dense cloud of millions of points with known positional coordinates. The millimeter-level precision of the dataset enables the measurement of different features at a high degree of accuracy. Big data sets are complex, making processing and extracting information a challenge using existing tools and techniques. However, the rich amounts of information that can be extracted from big LiDAR data have the potential to revolutionize the decision-making process in transportation engineering. Consequently, this DG is divided into multiple phases. In the first phase, algorithms are developed for the extraction and assessment of geometric features of highways. Phase two focuses on using the extracted data to allow for a large-scale adaptation of a performance-based design approach. This involves assessing the geometric performance of each highway segment to identify how well they satisfy design standards or alternately fail to meet design standards. The performance-based assessment is conducted with the aim of understanding the underlying links between the demand for geometric integrity and safety performance. Finally, the last phase explores how the developed algorithms inform infrastructure planning and management processes. The anticipated research is expected to have significant impacts in the fields of highway engineering while creating research opportunities in the areas of infrastructure management and planning as well as highway design. By processing big LiDAR datasets using the proposed methods, information, often challenging to obtain using conventional surveying tools, is made readily available at an unprecedented scale and speed. This helps transportation agencies enrich their databases with detailed information about important roadway features and design elements, which, in turn, result in a more efficient and economic infrastructure asset management process. The additional information made available due to this research will also help researchers better understand the relationship between geometric integrity and safety performance of different highway elements. The ultimate contribution of this research is facilitating the means by which the process of transportation infrastructure management and highway design could be transformed into one that is more inclusive, informative and evidence-based.

这项发现补助金（DG）的重点是开发有效和准确处理大型LiDAR数据集的方法，最终目标是帮助基础设施管理和公路设计流程更加知情和数据驱动。光探测和测距（LiDAR）技术就是这样一种遥感技术，它有可能改变公路工程的测量过程。在单次勘测过程中，移动的LiDAR设备可以以具有已知位置坐标的数百万个点的高密度云的形式创建公路基础设施的精确三维表示。数据集的毫米级精度使不同特征的测量具有很高的精度。大数据集非常复杂，使用现有工具和技术处理和提取信息是一项挑战。然而，可以从大型LiDAR数据中提取的丰富信息有可能彻底改变交通工程的决策过程。 因此，该DG分为多个阶段。在第一阶段中，算法的提取和评估的几何特征的公路。第二阶段的重点是使用提取的数据，以允许一个基于性能的设计方法的大规模适应。这涉及到评估每个公路段的几何性能，以确定它们满足设计标准的程度，或者不满足设计标准的程度。进行基于性能的评估的目的是了解几何完整性和安全性能的需求之间的潜在联系。最后，最后一个阶段探讨了开发的算法如何为基础设施规划和管理流程提供信息。 预期的研究预计将在公路工程领域产生重大影响，同时在基础设施管理和规划以及公路设计领域创造研究机会。通过使用所提出的方法处理大型LiDAR数据集，可以以前所未有的规模和速度随时获得使用传统测量工具通常具有挑战性的信息。这有助于运输机构利用有关重要道路特征和设计元素的详细信息丰富其数据库，从而实现更高效、更经济的基础设施资产管理流程。本研究提供的额外信息也将有助于研究人员更好地了解不同公路要素的几何完整性和安全性能之间的关系。这项研究的最终贡献是促进交通基础设施管理和公路设计的过程可以转化为一个更具包容性，信息和循证的手段。