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

Youn, Junshee; Bethel, James; Mikhail, Edward M.; Lee, Changno

doi:10.14358/pers.74.2.227

Cited by 37 publications

(13 citation statements)

References 8 publications

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“…Hinz and Baumgartner (2003) have developed a detailed heuristic model for roads and their context in scale-space, using evidence from multiple overlapping aerial images. More recently, Youn et al (2008) combine an orthophoto and airborne laser scanning data to extract wide, largely unoccluded roads that follow a grid pattern. Similar to Hinz and Baumgartner (2003) and Grote et al (2012) they design a hierarchical framework which constructs longer road pieces from initial segments, but no high-level connectivity is imposed, thus many gaps remain.…”

Section: Related Workmentioning

confidence: 99%

Road networks as collections of minimum cost paths

Wegner

Montoya-Zegarra

Schindler

2015

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Related Workmentioning

confidence: 99%

Road networks as collections of minimum cost paths

Wegner

Montoya-Zegarra

Schindler

2015

ISPRS Journal of Photogrammetry and Remote Sensing

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“…(Ziems et al, 2011b). Intersection: This module is based on the method developed by Youn et al (2008) for road extraction in urban areas. The underlying model is based on the structural differences between a road and a row of buildings.…”

Section: Verification Modulesmentioning

confidence: 99%

“…The confidence C build is defined on the basis of a reliability map originated from the nDSM generation process. Grassland: In (Zhang, 2004) and (Youn et al, 2008), grassland was considered as a hint against the existence of a road. As the reconstruction of a road is frequently connected with a redevelopment of grassland areas it is usually a good indicator for such a change (e.g.…”

Section: Verification Modulesmentioning

confidence: 99%

Multiple-Model Based Verification of Road Data

Ziems

Breitkopf

Heipke

et al. 2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper describes a semi-automatic system for road verification based on high resolution imagery and 3D surface models. Potential update regions are identified by an object-wise verification of all existing database records. The proposed system combines several road detection and road verification approaches from current literature to form a more general solution. Each road detection / verification approach is realized as an independent module representing a unique road model combined with a corresponding processing strategy. The object-wise verification result of each module is formulated as a binary decision between the classes "correct road" and "incorrect road". These individual decisions are combined by Dempster-Shafer fusion, which provides tools for dealing with uncertain and incomplete knowledge about the statistical properties of the data. For each road detection / verification module a confidence function for the result is introduced that reflects the degree of correspondence of an actual test situation with an optimal situation according to the underlying road model of that module. A comparison with results from an EuroSDR test on road extraction demonstrate the strengths and limitations of the method.

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“…This module is based on the method developed by Youn et al (2008) for the task of road extraction in urban areas, where the buildings form regular grids. The underlying model is based on the structural differences between a road and a row of buildings.…”

Section: Edge Direction Analysismentioning

confidence: 99%

“…As the raw data of the matching process and the camera orientations are not available for the task, the confidence function is only based on the height h of the intersecting building and the road width w, stored in the database. The confidence C build is defined as follows: Grassland Detection: In (Zhang, 2004) and (Youn et al, 2008) grassland was considered as a hint against the existence of a road. As changes concerning the road network are frequently connected with a redevelopment of grassland areas it is usually a good indicator for changes, exemplarily shown in Figure 6.…”

Section: Edge Direction Analysismentioning

confidence: 99%

Multiple-Model Based Update of Belgian Reference Road Data

Ziems

Beyen²,

Mueller

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper describes a semi-automatic system for road update based on high resolution orthophotos and 3D surface models. Potential update regions are identified by an object-wise verification of all existing database records, followed by a scene-wide detection of redevelopment regions. The proposed system combines several road detection and road verification approaches from current literature to form a more general solution. Each road detection / verification approach is realized as an independent module representing a unique road model combined with a corresponding processing strategy. The object-wise verification result of each module is formulated as a binary decision between the classes "correct road" and "incorrect road". These individual decisions are combined by Dempster-Shafer fusion, which provides tools for dealing with uncertain and incomplete knowledge about the statistical properties of the data. For each road detection / verification module a confidence function for the result is introduced that reflects the degree of correspondence of an actual test situation with an optimal situation according to the underlying road model of that module. Experimental results achieved with data from the national Belgian road database in a test site of about 134 km² demonstrate the potential of the method.

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

Cited by 37 publications

References 8 publications

Road networks as collections of minimum cost paths

Road networks as collections of minimum cost paths

Multiple-Model Based Verification of Road Data

Multiple-Model Based Update of Belgian Reference Road Data

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