Verification of road databases using multiple road models

Ziems, Marcel; Rottensteiner, Franz; Heipke, Christian

doi:10.1016/j.isprsjprs.2017.05.005

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…Following feature extraction, shallow models are employed with manually designed features. Typical shallow models include decision trees, support vector machines (SVM), Hough forests, tensor voting [64][65][66][67], etc. Movaghati et al handle road extraction as a tracking problem by adopting manually designed features [68].…”

Section: Traditional Approaches For Road Extraction From Hrsismentioning

confidence: 99%

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

et al. 2023

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Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network’s ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss (LSSIM) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs.

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Section: Traditional Approaches For Road Extraction From Hrsismentioning

confidence: 99%

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

et al. 2023

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“…However, there are two main points of hierarchical rasterization: (1) According to the selected area, the larger the area, the greater the amount of grading, and the minor area level must be Cell; the Grid between Cell and Div can be divided into multi-level according to the area. (2) The Cell size needs to be controlled; too much detailed information is easy to lose, and too small a neighborhood lane will increase the difficulty of centerline extraction.…”

Section: Acquisition and Hierarchical Rasterization Of Trajectory Datamentioning

confidence: 99%

“…With the rapid development of urban road construction, people's demand for travel is increasing, but road network information is updated slowly. The vector road network has high application value in land space planning, intelligent transportation, urban construction, and disaster relief [1,2]. The traditional road network acquisition methods are manual surveying and mapping, geographic information collection, and satellite image processing.…”

Section: Introductionmentioning

confidence: 99%

Research on Urban Road Network Extraction Based on Web Map API Hierarchical Rasterization and Improved Thinning Algorithm

Wen

Zhang

2022

Sustainability

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Most existing research on the vector road network is based on GPS trajectory travel information extraction, and urban GPS trajectory data are large and difficult to obtain. Based on this, this study proposes a road network extraction method based on network map API and designs a vector road network based on an improved image-processing algorithm using trajectory data. Firstly, a large number of trajectory data are processed by hierarchical rasterization. The trajectory points of the regional OD matrix are obtained by using the map API interface to generate the trajectory. Then, the image expansion processing is performed on the road network raster image to complete the information loss problem. The improved Zhang–Suen refinement algorithm is used to refine the idea to obtain the road center line, and the vector road network in the study area is obtained. Finally, taking the Harbin City of Heilongjiang Province as an example, compared with the road network of the network map, it has been demonstrated that using this technology may improve the traveler experience and the sustainability of urban traffic flow while reducing the number of manual procedures required, performing online incremental rapid change detection, and updating the present road network at a cheaper cost.

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“…Considering the following aspects of software engineering, database architecture designing requires the following [10][11][12][13][14][15][16][17][18]:…”

Section: Database Development Solutions For Logistics-related Applicamentioning

confidence: 99%

An Integrated EPLOS Database as a Tool Supporting TSL Companies

Wasiak

Kłodawski

Lewczuk

et al. 2020

Teh. glas. (Online)

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The paper presents the conceptual design of a database for the European Portal of Logistics Services (EPLOS) and its application. The database contains the data on logistics companies, the infrastructure for road, railway, inland, and air transport, as well as the data on the nodal elements of logistics infrastructure (warehouse facilities, seaports, transhipment terminals). Complete and verified information is the fundamental condition for rational decisions about the realization of logistics processes on a meso- and macroeconomic scale. Authors present the relations in the making of the EPLOS database, its assumed scope, and the potential benefits for the TSL market from accessing the EPLOS database.

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Verification of road databases using multiple road models

Cited by 13 publications

References 36 publications

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

Research on Urban Road Network Extraction Based on Web Map API Hierarchical Rasterization and Improved Thinning Algorithm

An Integrated EPLOS Database as a Tool Supporting TSL Companies

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