Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105630
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Peng, Z | - |
dc.creator | Gao, S | - |
dc.creator | Xiao, B | - |
dc.creator | Guo, S | - |
dc.creator | Yang, Y | - |
dc.date.accessioned | 2024-04-15T07:35:32Z | - |
dc.date.available | 2024-04-15T07:35:32Z | - |
dc.identifier.issn | 1545-5955 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105630 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Z. Peng, S. Gao, B. Xiao, S. Guo and Y. Yang, "CrowdGIS: Updating Digital Maps via Mobile Crowdsensing," in IEEE Transactions on Automation Science and Engineering, vol. 15, no. 1, pp. 369-380, Jan. 2018 is available at https://doi.org/10.1109/TASE.2017.2761793. | en_US |
dc.subject | Digital map update | en_US |
dc.subject | Mobile crowdsensing | en_US |
dc.title | CrowdGIS : updating digital maps via mobile crowdsensing | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 369 | - |
dc.identifier.epage | 380 | - |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1109/TASE.2017.2761793 | - |
dcterms.abstract | Accurate digital maps play a crucial role in various location-based services and applications. However, store information is usually missing or outdated in current maps. In this paper, we propose CrowdGIS, an automatic store selfupdating system for digital maps that leverages street views and sensing data crowdsourced from mobile users. We first develop a new weighted artificial neural network to learn the underlying relationship between estimated positions and real positions to localize user's shooting positions. Then, a novel text detection method is designed by considering two valuable features, including the color and texture information of letters. In this way, we can recognize complete store name instead of individual letters as in the previous study. Furthermore, we transfer the shooting position to the location of recognized stores in the map. Finally, CrowdGIS considers three updating categories (replacing, adding, and deleting) to update changed stores in the map based on the kernel density estimate model. We implement CrowdGIS and conduct extensive experiments in a real outdoor region for 1 month. The evaluation results demonstrate that CrowdGIS effectively accommodates store variations and updates stores to maintain an up-to-date map with high accuracy. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on automation science and engineering, Jan. 2018, v. 15, no. 1, p. 369-380 | - |
dcterms.isPartOf | IEEE transactions on automation science and engineering | - |
dcterms.issued | 2018-01 | - |
dc.identifier.scopus | 2-s2.0-85033665540 | - |
dc.identifier.eissn | 1558-3783 | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-1015 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; HK PolyU | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6797111 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Peng_Crowdgis_Updating_Digital.pdf | Pre-Published version | 4.66 MB | Adobe PDF | View/Open |
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