Automatic vectorization of segmented road networks by geometrical and topological analysis of high resolution binary images

Mena, J. B.

doi:10.1016/j.knosys.2006.05.008

Cited by 33 publications

(15 citation statements)

References 15 publications

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“…Since every concavity of B causes one polyline, discarding those road segments which exhibit a suspicious geometric appearance (too short, too broad, etc.) and at the same time do not contribute to the topological functionality of the road net has been proposed in the literature (Mena, 2006;Bulatov et al, 2016b). Thus, the iterative filtering procedure is based on polyline attributes, such as width, length, type, etc., which are calculated according to Bulatov et al (2016a).…”

Section: Vectorization and Extraction Of Road Polylinesmentioning

confidence: 94%

“…There are several contributions related to generalization of road networks but for most of them, Chaudhry and Mackaness (2006) for example, data noise is not a significant problem. Our work is more similar to (Bulatov et al, 2016b;Mena, 2006), where segments are extracted from the actual sensor data and finally generalized either by the well-known algorithm of Douglas and Peucker (1973) or by higher order, e. g., Bézier curves. Both modules (Douglas-Peucker and Bézier curves) were modified in the way that the polygonal chains do not cross obstacles, such as buildings and trees.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

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Chain-Wise Generalization of Road Networks Using Model Selection

Bulatov

Wenzel

Häufel

et al. 2017

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

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ABSTRACT:Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.

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Section: Vectorization and Extraction Of Road Polylinesmentioning

confidence: 94%

Section: Introduction and Previous Workmentioning

confidence: 99%

Chain-Wise Generalization of Road Networks Using Model Selection

Bulatov

Wenzel

Häufel

et al. 2017

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

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show abstract

“…Morphological operators are employed as a postprocessing step to smooth the contour of detected line features [195]. Morphological operators [73,75,79,115,116,120,121,126,128,146,148,196,198,199] [132] [124,125,142,144,200] Remote Sens. 2016, 8, 689…”

Section: Postprocessingmentioning

confidence: 99%

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

et al. 2016

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Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step-including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.

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“…Here, not only some training data, but also a better classification of pixels should improve the situation significantly. Finally, we mention skeletonization and medial-axis-based methods as in (Gerke and Heipke, 2008, Mena, 2006, Miao et al, 2013 where fitting of a smooth curve is aimed, and (Noris et al, 2013), where minimal spanning trees of points near the centerline are analyzed; these points on the centerlines are obtained by clustering of salient (e.g. with respect to their gradient) pixels.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…with respect to their gradient) pixels. In (Mena, 2006), starting from the binary image R representing road pixels, the road centerlines are supposed to have the same distance to at least two points of R. This method is very sensitive to the classification results. Every concavity in R leads first to short, superfluous segments and, additionally, to zigzag-like, improbable street courses.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Vectorization of Road Data Extracted From Aerial and Uav Imagery

Bulatov¹,

Häufel²,

Pohl³

2016

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

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ABSTRACT:Road databases are essential instances of urban infrastructure. Therefore, automatic road detection from sensor data has been an important research activity during many decades. Given aerial images in a sufficient resolution, dense 3D reconstruction can be performed. Starting at a classification result of road pixels from combined elevation and optical data, we present in this paper a fivestep procedure for creating vectorized road networks. These main steps of the algorithm are: preprocessing, thinning, polygonization, filtering, and generalization. In particular, for the generalization step, which represents the principal area of innovation, two strategies are presented. The first strategy corresponds to a modification of the Douglas-Peucker-algorithm in order to reduce the number of vertices while the second strategy allows a smoother representation of street windings by Bezir curves, which results in reduction -to a decimal power -of the total curvature defined for the dataset. We tested our approach on three datasets with different complexity. The quantitative assessment of the results was performed by means of shapefiles from OpenStreetMap data. For a threshold of 6 m, completeness and correctness values of up to 85% were achieved.

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Automatic vectorization of segmented road networks by geometrical and topological analysis of high resolution binary images

Cited by 33 publications

References 15 publications

Chain-Wise Generalization of Road Networks Using Model Selection

Chain-Wise Generalization of Road Networks Using Model Selection

Review of Automatic Feature Extraction from High-Resolution Optical Sensor Data for UAV-Based Cadastral Mapping

Vectorization of Road Data Extracted From Aerial and Uav Imagery

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