Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Zhang, Zhiqiang; Zhang, Xinchang; Sun, Ying; Zhang, Pengcheng

doi:10.3390/rs10081284

Cited by 35 publications

(20 citation statements)

References 51 publications

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“…The traffic road network is one of the essential geographic element of the urban system, which has critical applications in many fields, such as intelligent transportation, automobile navigation, and emergency support [1]. With the development of remote sensing technology and the advancement of remote sensing data processing methods, high temporal and spatial resolution, remote sensing data can provide high-precision ground information and permit the large-scale monitoring of roads.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

et al. 2019

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Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

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Section: Introductionmentioning

confidence: 99%

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

et al. 2019

Self Cite

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“…The semantic segmentation of roads is a very challenging task. Unlike the extraction of road skeleton information [3][4][5], each pixel belonging to a road needs to be labeled as a road, and the remaining should be labeled as a background. This belongs to the problem of binary semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…(4) The extracted road is obscured by trees, buildings, shadows. (5) The complexity of the topological connectivity is reflected in the intersection and connectivity of multiple roads, which is a challenge for accurate road extraction. These factors make it difficult to extract roads from remote sensing images and also make the applicability of many semantic segmentation methods weak.…”

Section: Introductionmentioning

confidence: 99%

HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information

Miao

Zhou

Peng

2019

IJGI

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Road extraction is a unique and difficult problem in the field of semantic segmentation because roads have attributes such as slenderness, long span, complexity, and topological connectivity, etc. Therefore, we propose a novel road extraction network, abbreviated HsgNet, based on high-order spatial information global perception network using bilinear pooling. HsgNet, taking the efficient LinkNet as its basic architecture, embeds a Middle Block between the Encoder and Decoder. The Middle Block learns to preserve global-context semantic information, long-distance spatial information and relationships, and different feature channels’ information and dependencies. It is different from other road segmentation methods which lose spatial information, such as those using dilated convolution and multiscale feature fusion to record local-context semantic information. The Middle Block consists of three important steps: (1) forming a feature resource pool to gather high-order global spatial information; (2) selecting a feature weight distribution, enabling each pixel position to obtain complementary features according to its own needs; and (3) inversely mapping the intermediate output feature encoding to the size of the input image by expanding the number of channels of the intermediate output feature. We compared multiple road extraction methods on two open datasets, SpaceNet and DeepGlobe. The results show that compared to the efficient road extraction model D-LinkNet, our model has fewer parameters and better performance: we achieved higher mean intersection over union (71.1%), and the model parameters were reduced in number by about 1/4.

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“…The remote sensing images are color-scale images with complex background [40]. However, the trajectory feature graphs are gray-scale images with road and non-road information.…”

Section: B Road Feature Extraction From Rs Imagementioning

confidence: 99%

“…Morphology thinning is commonly used in extracting road centerlines from road area [33]. It is fast and easy to perform, especially suitable for extracting road centerlines in road areas with regular boundaries [40]. However, there are some discontinuous roads in initial road area results, because some road segments are not extracted either by trajectory or RS.…”

Section: A Road Centerline Extractionmentioning

confidence: 99%

Fusing Taxi Trajectories and RS Images to Build Road Map via DCNN

Xiang

Cai-li

et al. 2019

IEEE Access

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Accurate road maps are fundamental to a wide range of applications, such as navigation, transportation and urban planning. With the rapid development of Globe Position System (GPS) and Remote Sensing (RS), abundant and continuously updated spatiotemporal data are available for road map building. However, using single data source inevitably results in limited performance on road information recognition, due to inherent defects in data, such as the noise, low sampling rate and uneven density distribution in trajectories, and occluded roads in RS-images. This paper aims to integrate GPS trajectories and RS-images to build road map, which is composed of intersections and links. First, both dynamic and static trajectory characteristics are utilized to capture road features indicated in trajectories, and meanwhile, road features implied in by RS-images are extracted via transfer-learning with an advanced deep neural network. Then, the two distinct types of road features are explored together to learn the potential area of road centerlines based on a variation of U-Net, which refines the U-Net to incorporate the structure characters of road. Finally, a multistep refinement is introduced to extract road centerlines through morphology processing, then derive intersections through connectivity analysis and detect links based on sliding-window. In addition, inspired by vector rasterization, a fast and automatic strategy is developed to create large-scale road dataset to significantly reduce labor and time. Compared with three state-of-the-art segment networks, the proposed network achieves the highest correctness and quality. The experimental results demonstrate that trajectory data and RS image complement each other, which assure the extracted road network possesses both integrity and connectivity. INDEX TERMS Road map generation, crowdsourcing trajectory data, remote sensing image, data fusion.

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Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Cited by 35 publications

References 51 publications

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information

Fusing Taxi Trajectories and RS Images to Build Road Map via DCNN

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