Volume II-3/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 239-246, 2015
https://doi.org/10.5194/isprsannals-II-3-W4-239-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 239-246, 2015
https://doi.org/10.5194/isprsannals-II-3-W4-239-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Mar 2015

11 Mar 2015

ROAD AND ROADSIDE FEATURE EXTRACTION USING IMAGERY AND LIDAR DATA FOR TRANSPORTATION OPERATION

S. Ural1,2, J. Shan1, M. A. Romero1, and A. Tarko1 S. Ural et al.
  • 1Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA
  • 2Dept. of Geomatics Engineering, Hacettepe University, 06800, Beytepe, Ankara, Turkey

Keywords: Imagery, LiDAR, remote sensing, graph cuts, road extraction, transportation

Abstract. Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.