State of the art on automatic road extraction for GIS update: a novel classification

Mena, J. B.

doi:10.1016/s0167-8655(03)00164-8

Cited by 365 publications

(182 citation statements)

References 93 publications

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“…Already in (Mena, 2003) more than 250 different references were listed, while a more recent overview is given in (Poullis and You, 2009). However, only a minor part of these works deals with updating databases, which can be seen as a natural application for road detection methods.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…For road extraction from satellite and aerial images, a variety of methods have been proposed, using cues Email addresses: e-mail: aboyko@cs.princeton.edu (Aleksey Boyko), e-mail: funk@cs.princeton.edu (Thomas Funkhouser) based mainly on color, monochromatic intensity and texture patterns (Fortier et al, 1999;Mena, 2003). For example, common algorithmic strategies include region growing (Amo et al, 2006;Bicego et al, 2003;Hu et al, 2007;Mena and Malpica, 2005;Tesser and Pavlidis, 2000), segmentation and clustering (Ferchichi and Wang, 2005;Wan et al, 2007), machine learning (Butenuth et al, 2003;Yager and Sowmya, 2003), multi-scale extraction and refinement (Baumgartner and Hinz, 2000;Heipke et al, 1995;Mayer et al, 1998;Steger, 1998), and active contours (Laptev et al, 2000;Peng et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Extracting roads from dense point clouds in large scale urban environment

Boyko

Funkhouser

2011

ISPRS Journal of Photogrammetry and Remote Sensing

151

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This paper describes a method for extracting roads from a large scale unstructured 3D point cloud of an urban environment consisting of many superimposed scans taken at different times. Given a road map and a point cloud, our system automatically separates road surfaces from the rest of the point cloud. Starting with an approximate map of the road network given in the form of 2D intersection locations connected by polylines, we first produce a 3D representation of the map by optimizing Cardinal splines to minimize the distances to points of the cloud under continuity constraints. We then divide the road network into independent patches, making it feasible to process a large point cloud with a small in-memory working set. For each patch, we fit a 2D active contour to an attractor function with peaks at small vertical discontinuities to predict the locations of curbs. Finally, we output a set of labeled points, where points lying within the active contour are tagged as "road" and the others are not. During experiments with a LIDAR point set containing almost a billion points spread over six square kilometers of a city center, our method provides 86% correctness and 94% completeness.

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“…A semiautomatic road tracker based on road profile correlation and road edge following for aerial images was proposed in [13]. The tracker was initialized by the user to obtain starting for position, direction and width of the road.…”

Section: Introductionmentioning

confidence: 99%