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A Framework for Interactive Geospatial Map Cleaning using GPS Trajectories

Published: 07 November 2017 Publication History
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  • Abstract

    A volunteered geographic information system, e.g., OpenStreetMap (OSM), collects data from volunteers to generate geospatial maps. To keep the map consistent, volunteers are expected to perform the tedious task of updating the underlying geospatial data at regular intervals. Such map curation step takes time and considerable human effort. In this paper, we propose a framework that improves the process of updating geospatial maps by automatically identifying road changes from user generated GPS traces. Since GPS traces can be sparse and noisy, the proposed framework validates the map changes with the users before propagating them to a publishable version of the map. The proposed framework achieves up to four times faster map matching performance than the state-of-the-art algorithms with only 0.1--0.3% accuracy loss.

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    Cited By

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    • (2021)(Big) Data in Urban Design Practice: Supporting High-Level Design Tasks Using a Visualization of Human Movement Data from SmartphonesUrban Informatics and Future Cities10.1007/978-3-030-76059-5_16(301-318)Online publication date: 16-Jul-2021

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    cover image ACM Conferences
    IWCTS'17: Proceedings of the 10th ACM SIGSPATIAL Workshop on Computational Transportation Science
    November 2017
    46 pages
    ISBN:9781450354912
    DOI:10.1145/3151547
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 07 November 2017

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    Author Tags

    1. Map Cleaning
    2. Map Matching Algorithm

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    Overall Acceptance Rate 42 of 57 submissions, 74%

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    • (2021)(Big) Data in Urban Design Practice: Supporting High-Level Design Tasks Using a Visualization of Human Movement Data from SmartphonesUrban Informatics and Future Cities10.1007/978-3-030-76059-5_16(301-318)Online publication date: 16-Jul-2021

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