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  "author": "Zourlidou",
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  "author": " S. Zourlidou",
            
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  "author_email": "stefania.zourlidou@ikg.uni-hannover.de",
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  "author_email": "stefania.zourlidou@ikg.uni-hannover.de",
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "doi": "10.25835/v0mzwob3",
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  "doi": "10.25835/v0mzwob3",
            
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  "doi_date_published": "2022-08-01",
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  "doi_date_published": "2022-08-01",
            
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  "doi_publisher": "LUIS",
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  "doi_publisher": "LUIS",
            
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  "doi_status": "true",
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  "doi_status": "true",
            
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  "domain": "https://data.uni-hannover.de",
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  "domain": "https://data.uni-hannover.de",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": " S."
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    },

            
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    {

            
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      "extra_author": " Golze"
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      "extra_author": "  J. Golze",
            
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": " J. and Sester"
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      "extra_author": " M. Sester",
            
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    },
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      "orcid": ""
            
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    {

            
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      "extra_author": " M."

            
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    }
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  ],
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  "groups": [],
            
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  "have_copyright": "Yes",
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  "have_copyright": "Yes",
            
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  "id": "bef17990-a1b8-4b4c-b31e-90fed37367a2",
            
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  "license_id": "CC-BY-NC-3.0",
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  "license_id": "",
            
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  "license_title": "CC-BY-NC-3.0",
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  "license_title": "",
            
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  "maintainer": "Jens Golze",
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  "maintainer": "Jens Golze",
            
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  "maintainer_email": "jens.golze@ikg.uni-hannover.de",
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  "maintainer_email": "jens.golze@ikg.uni-hannover.de",
            
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  "metadata_created": "2023-01-12T13:14:29.307047",
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  "metadata_created": "2023-01-12T13:14:29.307047",
            
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  "metadata_modified": "2023-08-04T08:46:52.461839",
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  "metadata_modified": "2024-02-16T14:24:43.739694",
            
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  "name": 
30
  "name": 
            
35
tory-collection-and-intersection-ground-truth-information-for-edessa",
31
tory-collection-and-intersection-ground-truth-information-for-edessa",
            
36
  "notes": "This dataset is used for the classification of traffic 
32
  "notes": "This dataset is used for the classification of traffic 
            
37
intersections regulation rules using car GPS trajectories. In order to 
33
intersections regulation rules using car GPS trajectories. In order to 
            
38
apply supervised classification methods, the ground-truth information 
34
apply supervised classification methods, the ground-truth information 
            
39
is also contained in this dataset. The regulators are annotated based 
35
is also contained in this dataset. The regulators are annotated based 
            
40
on the intersection arms at each intersection.\r\n\r\nAn overview of 
36
on the intersection arms at each intersection.\r\n\r\nAn overview of 
            
41
the dataset's trajectories can be seen in the subsequent 
37
the dataset's trajectories can be seen in the subsequent 
            
42
figure:\r\n![Overview of the dataset containing the trajectories 
38
figure:\r\n![Overview of the dataset containing the trajectories 
            
43
collected in 
39
collected in 
            
44
fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png)\r\n\r\nAn 
40
fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png)\r\n\r\nAn 
            
45
overview of the related intersection regulator ground-truth rules can 
41
overview of the related intersection regulator ground-truth rules can 
            
46
be seen in the following 
42
be seen in the following 
            
47
-445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png)\r\n\r\n## 
43
-445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png)\r\n\r\n## 
            
48
Data Acquisition\r\nThe trajectory samples were recorded using an 
44
Data Acquisition\r\nThe trajectory samples were recorded using an 
            
49
android smartphone while driving a car in and around the city of 
45
android smartphone while driving a car in and around the city of 
            
50
Edessa, Greece. The acquisition period was from March 2018 to 
46
Edessa, Greece. The acquisition period was from March 2018 to 
            
51
September 2018 (6 month) by only a single person. The recording of the 
47
September 2018 (6 month) by only a single person. The recording of the 
            
52
trajectories has taken place without restrictions in order to reflect 
48
trajectories has taken place without restrictions in order to reflect 
            
53
a normal behavior of everyday car journeys. The sampling rate is 
49
a normal behavior of everyday car journeys. The sampling rate is 
            
54
approximately 1 sample per second.\r\nAdditionally to the GPS 
50
approximately 1 sample per second.\r\nAdditionally to the GPS 
            
55
trajectories, the ground-truth regulator rules of the traffic 
51
trajectories, the ground-truth regulator rules of the traffic 
            
56
intersections were annotated via street view images.\r\n\r\n## Related 
52
intersections were annotated via street view images.\r\n\r\n## Related 
            
57
Publications:\r\n* __Zourlidou, S., Sester, M. and Hu, S. (2022):__ 
53
Publications:\r\n* __Zourlidou, S., Sester, M. and Hu, S. (2022):__ 
            
58
Recognition of Intersection Traffic Regulations From Crowdsourced 
54
Recognition of Intersection Traffic Regulations From Crowdsourced 
            
59
Data. Preprints 2022, 2022070012. DOI: 
55
Data. Preprints 2022, 2022070012. DOI: 
            
60
10.20944/preprints202207.0012.v1\r\n\r\n* __Zourlidou, S., Golze, J. 
56
10.20944/preprints202207.0012.v1\r\n\r\n* __Zourlidou, S., Golze, J. 
            
61
and Sester, M. (2022):__ Traffic Regulation Recognition using 
57
and Sester, M. (2022):__ Traffic Regulation Recognition using 
            
62
Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience 
58
Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience 
            
63
Ser., 3, 22, 2022. 
59
Ser., 3, 22, 2022. 
            
64
https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H., 
60
https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H., 
            
65
Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control 
61
Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control 
            
66
Recognition with an Attention Mechanism Using Speed-Profile and 
62
Recognition with an Attention Mechanism Using Speed-Profile and 
            
67
Satellite Imagery data. The International Archives of the 
63
Satellite Imagery data. The International Archives of the 
            
68
Photogrammetry, Remote Sensing and Spatial Information Sciences 
64
Photogrammetry, Remote Sensing and Spatial Information Sciences 
            
69
XLIII-B4-2022, S. 287\u201329. 
65
XLIII-B4-2022, S. 287\u201329. 
            
70
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n* 
66
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n* 
            
71
__Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 
67
__Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 
            
72
Trajectory analysis at intersections for traffic rule identification. 
68
Trajectory analysis at intersections for traffic rule identification. 
            
73
Geo-spatial Information Science, 11(4):1-10. 
69
Geo-spatial Information Science, 11(4):1-10. 
            
74
https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H., 
70
https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H., 
            
75
Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition 
71
Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition 
            
76
with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 
72
with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 
            
77
2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, 
73
2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, 
            
78
J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection 
74
J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection 
            
79
Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. 
75
Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. 
            
80
https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S., 
76
https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S., 
            
81
Fischer, C. and Sester, M. (2019):__  Classification of street 
77
Fischer, C. and Sester, M. (2019):__  Classification of street 
            
82
junctions according to traffic regulators. In: _Kyriakidis, P., 
78
junctions according to traffic regulators. In: _Kyriakidis, P., 
            
83
Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 
79
Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 
            
84
Accepted short papers and posters from the 22nd AGILE conference on 
80
Accepted short papers and posters from the 22nd AGILE conference on 
            
85
geo-information science. Cyprus University of Technology 17\u201320 
81
geo-information science. Cyprus University of Technology 17\u201320 
            
86
June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n* 
82
June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n* 
            
87
__Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS 
83
__Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS 
            
88
Trajectory Dataset of the Region of Hannover, Germany. 
84
Trajectory Dataset of the Region of Hannover, Germany. 
            
89
https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J. 
85
https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J. 
            
90
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
86
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            
91
Information for the Chicago Trajectory Dataset. 
87
Information for the Chicago Trajectory Dataset. 
            
92
https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J. 
88
https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J. 
            
93
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
89
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            
94
Information of the City of Hannover, Germany. 
90
Information of the City of Hannover, Germany. 
            
95
https://doi.org/10.25835/cqg0x1el \r\n\r\n* __Zourlidou, S., Golze, J. 
91
https://doi.org/10.25835/cqg0x1el \r\n\r\n* __Zourlidou, S., Golze, J. 
            
96
and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories 
92
and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories 
            
97
for Traffic Rule Recognition (6 Junctions, Hannover, Germany). 
93
for Traffic Rule Recognition (6 Junctions, Hannover, Germany). 
            
98
https://doi.org/10.25835/0043786",
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https://doi.org/10.25835/0043786",
            
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