Automated street corridor classification using mobile mapping system

Olcampus Corporation是北美领先的公用事业服务提供商，其加拿大分部位于安大略的奥罗拉。今年，Olecom的目标是开始开发一种新的大空间数据采集和分析服务，该服务将用于大规模库存管理和关键基础设施的快速健康监测，如公用事业线路和路面。为了支持这项研究，该公司最近推出了IP-S3，这是当今最紧凑的高清移动的地图系统，目前每天为该公司提供400公里的街道走廊数据。从IP-S3生成的如此大量的数据中进行特征提取和信息检索呈现出高度的处理复杂性并且需要大量的资源;然而，这项工作目前是手动执行的， 这通常是对时间和资源的低效使用。为了支持Olcraft新的大数据服务的开发，拟议的研究旨在研究自动计算机视觉方法，将IP-S3捕获的街道场景有效地分类为关键分类对象，这些对象将作为进一步生产矢量化基础设施地图的基础信息层。

Olameter Corporation is a leading provider of utility services in North America, with its Canadian division based in Aurora, Ontario. This year, Olameter aims to start development on a new big spatial data acquisition and analytics service, which will be used for large-scale inventory management and rapid healthiness monitoring of key infrastructure, such as utility lines and road surfaces. To support this research, the company recently introduced IP-S3, the most compact high-definition mobile mapping system available today, which currently provides the company with 400 km of street corridor data daily. Feature extraction and information retrieval from such a massive volume of data generated by IP-S3 presents a high degree of processing complexity and requires a large amount of resources; however, this work is currently performed manually, which is a generally inefficient use of time and resources. In support of the development of Olameter's new big data service, the proposed research intends to investigate automated computer vision methods of efficiently classifying street scenes captured by the IP-S3 into key categorized objects, which will serve as a base information layer for further production of a vectorized infrastructure map.