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CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL
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Zeitschriftentitel: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Personen und Körperschaften: | , |
In: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B2, 2016, S. 257-262 |
Format: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Copernicus GmbH
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Schlagwörter: |
author_facet |
Yang, X. Tang, L. Yang, X. Tang, L. |
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author |
Yang, X. Tang, L. |
spellingShingle |
Yang, X. Tang, L. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL General Earth and Planetary Sciences General Environmental Science |
author_sort |
yang, x. |
spelling |
Yang, X. Tang, L. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xli-b2-257-2016 <jats:p>Abstract. GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy. </jats:p> CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
doi_str_mv |
10.5194/isprs-archives-xli-b2-257-2016 |
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2016 |
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Copernicus GmbH |
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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title |
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_unstemmed |
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_full |
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_fullStr |
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_full_unstemmed |
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_short |
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_sort |
crowdsourcing big trace data filtering: a partition-and-filter model |
topic |
General Earth and Planetary Sciences General Environmental Science |
url |
http://dx.doi.org/10.5194/isprs-archives-xli-b2-257-2016 |
publishDate |
2016 |
physical |
257-262 |
description |
<jats:p>Abstract. GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy.
</jats:p> |
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author | Yang, X., Tang, L. |
author_facet | Yang, X., Tang, L., Yang, X., Tang, L. |
author_sort | yang, x. |
container_start_page | 257 |
container_title | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
container_volume | XLI-B2 |
description | <jats:p>Abstract. GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy. </jats:p> |
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source_id | 49 |
spelling | Yang, X. Tang, L. 2194-9034 Copernicus GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.5194/isprs-archives-xli-b2-257-2016 <jats:p>Abstract. GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy. </jats:p> CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
spellingShingle | Yang, X., Tang, L., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL, General Earth and Planetary Sciences, General Environmental Science |
title | CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_full | CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_fullStr | CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_full_unstemmed | CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_short | CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
title_sort | crowdsourcing big trace data filtering: a partition-and-filter model |
title_unstemmed | CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
topic | General Earth and Planetary Sciences, General Environmental Science |
url | http://dx.doi.org/10.5194/isprs-archives-xli-b2-257-2016 |