Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran.
Institute of Environment, University of Tabriz, Tabriz 5166616471, Iran.
Sensors (Basel). 2021 Mar 23;21(6):2251. doi: 10.3390/s21062251.
In this study, we measured the in situ international roughness index (IRI) for first-degree roads spanning more than 1300 km in East Azerbaijan Province, Iran, using a quarter car (QC). Since road quality mapping with in situ measurements is a costly and time-consuming task, we also developed new equations for constructing a road quality proxy map (RQPM) using discriminant analysis and multispectral information from high-resolution Sentinel-2 images, which we calibrated using the in situ data on the basis of geographic information system (GIS) data. The developed equations using optimum index factor (OIF) and norm R provide a valuable tool for creating proxy maps and mitigating hazards at the network scale, not only for primary roads but also for secondary roads, and for reducing the costs of road quality monitoring. The overall accuracy and kappa coefficient of the norm R equation for road classification in East Azerbaijan province are 65.0% and 0.59, respectively.
在这项研究中，我们使用四分之一车（QC）测量了伊朗东阿塞拜疆省超过 1300 公里的一级道路的原地国际粗糙度指数（IRI）。由于使用原地测量进行道路质量测绘是一项昂贵且耗时的任务，我们还开发了新的方程，使用判别分析和高分辨率 Sentinel-2 图像的多光谱信息构建道路质量代理图（RQPM），我们使用基于地理信息系统（GIS）数据的原地数据对其进行校准。使用最优指数因子（OIF）和规范 R 开发的方程为创建代理图和缓解网络规模的危险提供了有价值的工具，不仅适用于主要道路，也适用于次要道路，并降低了道路质量监测的成本。规范 R 方程在东阿塞拜疆省进行道路分类的总准确性和kappa 系数分别为 65.0%和 0.59。
