Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine

Wang, Jun; Song, Jingwei; Chen, Mingquan; Yang, Zhi

doi:10.1080/01431161.2015.1054049

Cited by 147 publications

(87 citation statements)

References 41 publications

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“…The evaluation results verify the efficiency of our method in terms of all three metrics and clearly show that it outperforms the other methods in this dataset. Focusing on the main roads, the result of our extraction approach is compared with the results of Poullis 2014 as well as two other methods proposed by Wang et al [15] and Poullis et al [27], which henceforth we cite them as "Wang 2015" and "Poullis 2010". In Table 2, completeness, correctness, and quality are listed for the results of all four methods.…”

Section: Second Experimentsmentioning

confidence: 99%

“…The selected part of the scene has 512ˆ512 pixels, with a "nominal" spatial resolution of 1 m and three spectral bands. With "nominal", we emphasize that this data originates from screen capturing of a Wikimapia image [10,13,15] and the original resolution of the recorded image might have been different. The image of the first dataset and its ground truth is depicted in Figure 5a,b, respectively.…”

Section: First Experimentsmentioning

confidence: 99%

“…Wang et al [15] proposed a semi-automatic neural-dynamic tracking framework which consists of two processing steps: the training step and the tracking step and is based on deep convolutional neural networks to recognize the pattern of input data and a finite state machine to translate the recognized patterns to states and control the behavior of the tracker.…”

Section: Introductionmentioning

confidence: 99%

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Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.

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Section: Second Experimentsmentioning

confidence: 99%

Section: First Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

et al. 2016

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“…Inspired from these approaches, we extract the urban elements of a particular area from satellite images using deep learning to capture their representative features. Similar approaches extract road networks using neural networks for dynamic environments [18] from LiDAR data [19], using line integrals [20] and using image processing approaches [21][22][23]. In our approach, to provide scalability across countries and terrains, we have explored and modified state-of-the-art image segmentation networks.…”

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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“…Unlike unsupervised learning, more than one features-other than color-can be extracted: line, shape, and texture, among others. The traditional deep learning methods such as deep convolutional neural networks (DCNN) [14] and [3], deep deconvolutional neural networks (DeCNN) [5], recurrent neural network, namely reSeg [15], and fully convolutional networks [4]. However, are all suffering from the accuracy performance issues.…”

Section: Introductionmentioning

confidence: 99%

Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

Panboonyuen¹,

Vateekul²,

Jitkajornwanich³

et al. 2017

Preprint

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Abstract:Object segmentation on remotely-sensed images: aerial (or very high resolution, VHS) images and satellite (or high resolution, HR) images, has been applied to many application domains, especially road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts in applying deep convolutional neural network (DCNN) to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction on remote sensing images by applying landscape metrics (LMs) and conditional random fields (CRFs). To improve DCNN, a modern activation function, called exponential linear unit (ELU), is employed in our network resulting in a higher number of and yet more accurate extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as THEOS satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, the state-of-the-art object segmentation technique on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F1.

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Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine

Cited by 147 publications

References 41 publications

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

Generative Street Addresses from Satellite Imagery

Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields

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