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Combining Images and Trajectories Data to Automatically Generate Road Networks
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Combining Images and Trajectories Data to Automatically Generate Road Networks
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Combining Images and Trajectories Data to Automatically Generate Road Networks
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Journal Article
Combining Images and Trajectories Data to Automatically Generate Road Networks
2023
Overview
Road network data are an important part of many applications, e.g., intelligent transportation and urban planning. At present, most of the approaches to road network generation are dominated by single data sources including images, point cloud data, trajectories, etc., which may cause the fragmentation of information. This study proposes a novel strategy to obtain the vector data of road networks by combining images and trajectory data with a postprocessing method named RNITP. The designed RNITP includes two parts: an initial generation layer of road network detection and a postprocessing layer of vector map acquirement. At the first layer, there are three steps of road network detection including road information interpretation from images based on a new deep learning model (denoted as SPBAM-LinkNet), road detection from trajectories data by rasterizing, and road information fusion by using OR operation. The last layer is used to generate a vector map based on a postprocessing method that is focused on error identification and removal. Experiments were conducted using two kinds of datasets: CHN6-CUG road datasets and HB road datasets. The results show that the accuracy, F1 score, and MIoU of SPBAM-LinkNet on CHN6-CUG and HB were (0.9695, 0.7369, 0.7760) and (0.9387, 0.7257, 0.7514), respectively, which are better than other typical models (e.g., Unet, DeepLabv3+, D-Linknet, NL-Linknet). In addition, the F1 score, IoU, and recall of the vector map obtained from RNITP are 0.8883, 0.7991, and 0.9065, respectively.
