Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints

Hu, Jiuxiang; Razdan, Anshuman; Femiani, John; Cui, Meiying; Wonka, Peter

doi:10.1109/tgrs.2007.906107

Cited by 282 publications

(141 citation statements)

References 33 publications

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“…Boichis et al assessed an interpretation strategy system for automatically extracting road intersections from aerial images [14]. Furthermore, Hu et al proposed a toe-finding algorithm based on rectangular approximations to generate a road network and road intersections from aerial images [15] and discussed classical road intersection types. Unfortunately, frequent cloud cover and complicated pre-treatments (e.g., geometrical rectification and image mosaics) can have a cumulatively negative effect and even result in extracted road networks with errors in their topological structures.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

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Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road networks in terms of connectivity. However, extracted intersections often present unsatisfactory precision and misleading connectivity. This study proposes a novel method for extracting road intersections from Global Position System (GPS) trace points and for capturing intersections with better accuracy. The key to improving the geometric accuracy of intersections is to identify the dominant orientations of road segments around intersections, merge similar orientations and maintain independent conflicting orientations. Extracting intersections by aligning the dominant orientations can largely reduce location offsets and road distortions. Experiments are performed to demonstrate the increased accuracy and connectivity of extracted road intersections by the proposed method.

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Section: Related Workmentioning

confidence: 99%

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

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“…For Test Site 1, the ground truth has a length of almost 520 m. The evaluation of the results was carried out by comparing the curbs extracted by the proposed method with the previously compiled ground truth. This was performed using three indices commonly used in the evaluation of road detection: completeness (Equation (4)), correctness (Equation (5)), and quality (Equation (6)) [34,35]:…”

Section: Test Site 1: Curb Representation and Accuracy Evaluationmentioning

confidence: 99%

Morphological Operations to Extract Urban Curbs in 3D MLS Point Clouds

Rodríguez-Cuenca

García-Cortés

Ordóñez

et al. 2016

IJGI

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Automatic curb detection is an important issue in road maintenance, three-dimensional (3D) urban modeling, and autonomous navigation fields. This paper is focused on the segmentation of curbs and street boundaries using a 3D point cloud captured by a mobile laser scanner (MLS) system. Our method provides a solution based on the projection of the measured point cloud on the XY plane. Over that plane, a segmentation algorithm is carried out based on morphological operations to determine the location of street boundaries. In addition, a solution to extract curb edges based on the roughness of the point cloud is proposed. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. The proposed method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. The extraction method provides completeness and correctness rates above 90% and quality values higher than 85% in both studied datasets.

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“…As an important part of the road network, intersection has many modeling methods, and its modeling data source is various. Some scholars [1]- [5] extract intersection data from aerial and remote sensing maps, with location, connected road segment, and road segment direction included. Some scholars [6]- [9] extract and update road network data based on massive GPS vehicle trajectory data.…”

Section: Introductionmentioning

confidence: 99%