A Deep Learning Method for Extracting Pedestrian Bridges Combining OSM and Remote Sensing Images

A technology for pedestrian bridges and remote sensing images, which is applied in character and pattern recognition, instruments, calculations, etc., can solve the problem that the identification results of pedestrian bridges cannot take into account the current situation and integrity of data at the same time, so as to improve the recognition efficiency and accuracy, reduce the subjectivity effect

Active Publication Date: 2019-10-25
CENT SOUTH UNIV
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Problems solved by technology

[0005] Aiming at the defects existing in the existing technology and solving the problem that the existing pedestrian bridge identification results cannot take into account the current situation and integrity of the data at the same time, the present invention provides a deep learning method for extracting pedestrian bridges by combining OSM and remote sensing images

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  • A Deep Learning Method for Extracting Pedestrian Bridges Combining OSM and Remote Sensing Images
  • A Deep Learning Method for Extracting Pedestrian Bridges Combining OSM and Remote Sensing Images
  • A Deep Learning Method for Extracting Pedestrian Bridges Combining OSM and Remote Sensing Images

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Embodiment 1

[0036] figure 1 It is a schematic flowchart of a deep learning method for extracting pedestrian bridges by combining OSM and remote sensing images according to an embodiment of the present invention. refer to figure 1 , the method includes:

[0037] S1. According to the semantic information, topological information and shape information of the pedestrian bridge in Open Street Map (OSM) data, automatically identify and extract the pedestrian bridge from the OSM data, specifically including the following steps:

[0038] S11. Semantic information modeling. OSM data contains rich semantic information, including road category information and subsidiary information. The subsidiary information of pedestrian bridges is "bridge", and the road category is "pedestrian road". Therefore, pedestrian bridges can be screened through OSM semantic information.

[0039] S12. Topological information modeling. The function of pedestrian bridges is to alleviate traffic congestion and provide co...

Embodiment 2

[0052] The specific implementation of the present invention will be described by using the OSM data of the main urban area of ​​Beijing in April 2016 and the corresponding 0.2m high-resolution remote sensing image data. The deep learning method for extracting pedestrian bridge targets by combining OSM and remote sensing images provided by the embodiments of the invention mainly includes identifying pedestrian bridges in OSM data, realizing semantic segmentation of pedestrian bridges in remote sensing image data, modeling of bridge structures and vector graphics, and pedestrian navigation data The update of Zhongtianqiao information. The specific implementation steps of the present invention to assist in solving problems related to pedestrian bridge identification and updating will be described below in conjunction with this example:

[0053] The flow of the pedestrian bridge method for identifying OSM data is as follows: figure 2 shown, including the following steps:

[005...

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Abstract

The invention discloses a deep learning method for extracting pedestrian bridge targets by combining OSM and remote sensing images. Firstly, according to the semantic information, topological information and shape information of the pedestrian bridge in the Open Street Map (OpenStreetMap, OSM), the pedestrian bridge is automatically identified and extracted from the OSM data; then the deep neural network based on image semantic segmentation The model acquires the outline of the pedestrian bridge; then performs structural modeling and vector graphics on the pedestrian bridge; finally updates the data of the pedestrian bridge in the pedestrian navigation system. The invention can not only automatically identify pedestrian bridges in OSM, reduce the subjectivity of existing methods, but also make up for the incompleteness of OSM by using remote sensing image data, while taking into account the timeliness and integrity of data, and improving the identification efficiency of pedestrian bridges and accuracy.

Description

technical field [0001] The invention belongs to the field of geographic element recognition and update, and in particular relates to a deep learning method for extracting pedestrian bridge targets by combining OSM and remote sensing images. Background technique [0002] The increasing demand for Pedestrian Navigation Service (PNS) makes the collection of pedestrian road data more and more important. As an integral part of the entire pedestrian road system, pedestrian bridges can alleviate the direct conflicts between traffic flow, pedestrian flow and the limited public transportation space in the city. Quick and easy access to pedestrian bridge data is not only the basis for improving pedestrian navigation systems, but also helps relevant departments (such as basic surveying and mapping departments) to complete the identification and update of geographic elements. [0003] At present, there are mainly two ways to obtain pedestrian bridge data, that is, using traditional sur...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46G06K9/62G06K9/38
CPCG06V10/28G06V10/44G06F18/214
Inventor 刘慧敏王晓路邓敏陈袁芳唐建波黄金彩
Owner CENT SOUTH UNIV
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