A Higher-Order CRF Model for Road Network Extraction

Wegner, Jan Dirk; Montoya-Zegarra, Javier A.; Schindler, Konrad

doi:10.1109/cvpr.2013.222

Cited by 170 publications

(109 citation statements)

References 26 publications

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“…In our approach, to provide scalability across countries and terrains, we have explored and modified state-of-the-art image segmentation networks. Finally, processing the road topology has been studied as an example case for novel or modified clustering and graph partitioning approaches [24][25][26]. Being a generative approach, our case differs from the previous cases by the ill-posed definition of "regions".…”

Section: Related Workmentioning

confidence: 99%

Generative Street Addresses from Satellite Imagery

Demir

Hughes

Raj

et al. 2018

IJGI

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Abstract:We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world's roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph-and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions.

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Section: Related Workmentioning

confidence: 99%

Generative Street Addresses from Satellite Imagery

Demir

Hughes

Raj

et al. 2018

IJGI

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“…where L is the length of the shorter line and R is the minimum intersection distance similar to d in Equation (13). Continuity: Continuity [23] describes the structural relationship among segments and determines the weight by which road segments should be connected, as shown in Figure 4e.…”

Section: Prior Constraints Under Beamlet Analysismentioning

confidence: 99%

“…CRF is based on the maximum entropy, having the advantage of achieving precise and robust labeled results. Wegner et al [13] developed a novel CRF formulation for road labeling, where the prior was represented by higher-order cliques aimed at describing the junctions and crossings in the structure of roads.…”

Section: Sar and Road Network Extractionmentioning

confidence: 99%

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Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

Fan

et al. 2017

IJGI

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This paper introduces an innovative road network extraction algorithm using synthetic aperture radar (SAR) imagery for improving the accuracy of road extraction. The state-of-the-art approaches, such as fraction extraction and road network optimization, failed to obtain continuous road segments in separate successions, since the optimization could not change the parts ignored by the fraction extraction. In this paper, the proposed algorithm integrates the fraction extraction and optimization procedure simultaneously to extract the road network: (1) the Bayesian framework is utilized to transfer the road network extraction to joint reasoning of the likelihood of fraction extraction and the priority of network optimization; (2) the multi-scale linear feature detector (MLFD) and the network optimization beamlet are introduced; (3) the conditional random field (CRF) is used to reason jointly. The result is the global optimum since the fraction extraction and network optimization are exploited at the same time. The proposed algorithm solves the problem that the fractions are bound to reduce in the process of network optimization and has demonstrated effectiveness in real SAR images applications.

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“…Due to the structure of this potential, a solution is found efficiently based on graph cuts. Wegner et al (2013) applied higher order potentials based on the P N -Potts model for the extraction of road networks from aerial images. In general, inference on higher order potentials is challenging, especially for generic formulations.…”

Section: Related Workmentioning

confidence: 99%

An Iterative Inference Procedure Applying Conditional Random Fields for Simultaneous Classification of Land Cover and Land Use

Albert

Rottensteiner

Heipke

2015

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

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ABSTRACT:Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a land cover layer and a land use layer. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relations between land cover and land use are integrated in the classification process by using contextual features. We propose a new iterative inference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutually influence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is that semantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentially wrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments are carried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inference procedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result.

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A Higher-Order CRF Model for Road Network Extraction

Abstract: Abstract

Cited by 170 publications

References 26 publications

Generative Street Addresses from Satellite Imagery

Generative Street Addresses from Satellite Imagery

Bayesian Fusion of Multi-Scale Detectors for Road Extraction from SAR Images

An Iterative Inference Procedure Applying Conditional Random Fields for Simultaneous Classification of Land Cover and Land Use

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