Extracting Urban Road Networks from High-resolution True Orthoimage and Lidar

Abstract

Automated or semi-automated feature extraction from remotely collected, large scale image data has been a challenging issue in digital photogrammetry for many years. In the feature extraction field, fusing different types of data to provide complementary information about the objects is becoming increasingly important. In this paper, we present a newly developed approach for the automatic extraction of urban area road networks from a true orthoimage and lidar assuming the road network to be a semi-grid pattern. The proposed approach starts from the subdivision of a study area into small regions based on homogeneity of the dominant road directions from the true orthoimage. Each region’s road candidates are selected with a proposed free passage measure. This process is called the “acupuncture” method. Features around the road candidates are used as key factors for an advanced “acupuncture method” called the region-based acupuncture method. Extracted road candidates are edited to avoid collocation with non-road features such as buildings and grass fields. In order to produce a building map for the prior step, a first-last return analysis and morphological filter are used with the lidar point cloud. A grass area thematic map is generated by supervised classification techniques from a synthetic image, which contains the three color bands from the true orthoimage and the lidar intensity value. Those non-road feature maps are used as a blocking mask for the roads. The accuracy of the result is evaluated quantitatively with respect to manually compiled road vectors, and a completeness of 80 percent and a correctness of 79 percent are obtained with the proposed algorithm on an area of 1,081,600 square meters.