Identification Of Unpaved Roads in a Regional Road Network Using Remote Sensing

Abstract

An accurate inventory of unpaved road network length and condition within a county, state, or region is important for efficient use of resources to manage and maintain this critical transportation asset. Object-based classification techniques provide a cost-effective way to identify unpaved roads within a local agency's road network when the road type (i.e., paved versus unpaved) attribute is missing. We present a Trimble eCognition® algorithm using four band optical aerial imagery and object-based classification to classify roads as paved or unpaved. The ruleset evaluates relationships between bands and allows separation and segmentation of unpaved roads from other pavement classes. The algorithm is applied to unincorporated areas of a six county region in Southeastern Michigan. Tree shadows on roads and the spectral similarity of road construction materials pose challenges to classification accuracy. An accuracy assessment of the classification indicated that the algorithm works well with overall classification accuracy between 82 and 94 percent.

The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images

“…This study has proven the role of remote sensing in facilitating the review and evaluation of road conditions and significant reduction of field data and introduced visible methods more tangible and cheaper than methods such as radar and terrestrial scanning. Brooks et al (2017) distinguished paved roads from unpaved ones in a regional road network using remote sensing and four-lane UAV images and object-based classification. The results of this study proved the relationship between the number of bands and the accuracy of separation, classification, and segmentation of paved and unpaved roads.…”

A Hydraulic Semi-Active Suspension Based on Road Statistical Properties and Its Road Identification

“…Their reliability, real-time, and implementation cost have some limits in application. The representative research works are as follows: Jokela et al used stereo cameras to monitor road conditions [26]; Nomura et al used acoustic equipment to measure the tire noise and identified the road condition by a neural network [27]; Alonso et al proposed an identification method based on a noise sensor and the support vector machine technology [28]; Paulo et al comprehensively used probability statistics and neural network to identify road types [29]; Brooks et al identified the unpaved roads by remote sensing technologies [30]; and Liu et al monitored the road levels through the engine speed [31]. These works play a role in promoting the realization of road identification technologies.…”