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CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL
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| Zeitschriftentitel: | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
|---|---|
| Personen und Körperschaften: | , |
| In: | ISPRS - 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
|
| author_facet |
Yang, X. Tang, L. Yang, X. Tang, L. |
|---|---|
| author |
Yang, X. Tang, L. |
| spellingShingle |
Yang, X. Tang, L. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
| author_sort |
yang, x. |
| spelling |
Yang, X. Tang, L. 2194-9034 Copernicus GmbH http://dx.doi.org/10.5194/isprsarchives-xli-b2-257-2016 <jats:p>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 ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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Copernicus GmbH, 2016 |
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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 |
| url |
http://dx.doi.org/10.5194/isprsarchives-xli-b2-257-2016 |
| publishDate |
2016 |
| physical |
257-262 |
| description |
<jats:p>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. |
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| container_start_page | 257 |
| container_title | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| container_volume | XLI-B2 |
| description | <jats:p>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> |
| doi_str_mv | 10.5194/isprsarchives-xli-b2-257-2016 |
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| imprint | Copernicus GmbH, 2016 |
| imprint_str_mv | Copernicus GmbH, 2016 |
| institution | DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161 |
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| physical | 257-262 |
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| series | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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| spelling | Yang, X. Tang, L. 2194-9034 Copernicus GmbH http://dx.doi.org/10.5194/isprsarchives-xli-b2-257-2016 <jats:p>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 ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spellingShingle | Yang, X., Tang, L., ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL |
| 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 |
| url | http://dx.doi.org/10.5194/isprsarchives-xli-b2-257-2016 |