Changes

On November 28, 2024 at 12:52:21 PM UTC, admin:

Set author of Traffic Regulator Ground-truth Information of the City of Hannover, Germany to Zourlidou, Stefania (previously S. Zourlidou)

Updated description of Traffic Regulator Ground-truth Information of the City of Hannover, Germany from
This dataset contains the ground-truth intersection regulators for a majority of intersections of the city of Hannover, Germany. The ground-truth information is used in order to apply machine learning techniques on (car) GPS trajectory data in order to automatically detect the intersection regulation. ![Rules](https://data.uni-hannover.de/dataset/1123552a-7946-4924-bbbc-aa7fbc6a800f/resource/0d5185cf-1a67-4374-97c7-397b65dad394/download/hannover_rules_1.png) The GPS trajectories related to specifically this dataset are (also) available under: https://doi.org/10.25835/9bidqxvl ## Data Acquisition The ground-truth information are acquired by visiting them on-site and apply manual labeling of each intersection arm individually. Furthermore, satellite images and street-level images were considered but only on a minor degree as on-site labeling is found to be more precise and up-to-date. ## Related Publications: * __Zourlidou, S., Sester, M. and Hu, S. (2022):__ Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: 10.20944/preprints202207.0012.v1 * __Zourlidou, S., Golze, J. and Sester, M. (2022):__ Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022 * __Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022 * __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374 * __Cheng, H., Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652 * __Golze, J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x * __Zourlidou, S., Fischer, C. and Sester, M. (2019):__ Classification of street junctions according to traffic regulators. In: _Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus. ## Related Datasets: * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3 * __Zourlidou, S., Golze, J. and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786
to
This dataset contains the ground-truth intersection regulators for a majority of intersections of the city of Hannover, Germany. The ground-truth information is used in order to apply machine learning techniques on (car) GPS trajectory data in order to automatically detect the intersection regulation. ![Rules](https://data.uni-hannover.de/dataset/1123552a-7946-4924-bbbc-aa7fbc6a800f/resource/0d5185cf-1a67-4374-97c7-397b65dad394/download/hannover_rules_1.png) The GPS trajectories related to specifically this dataset are (also) available under: https://doi.org/10.25835/9bidqxvl ## Data Acquisition The ground-truth information are acquired by visiting them on-site and apply manual labeling of each intersection arm individually. Furthermore, satellite images and street-level images were considered but only on a minor degree as on-site labeling is found to be more precise and up-to-date. ## Related Publications: * __Zourlidou, S., Sester, M. and Hu, S. (2022):__ Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1 * __Zourlidou, S., Golze, J. and Sester, M. (2022):__ Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022 * __Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022 * __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374 * __Cheng, H., Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652 * __Golze, J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x * __Zourlidou, S., Fischer, C. and Sester, M. (2019):__ Classification of street junctions according to traffic regulators. In: _Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus. ## Related Datasets: * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3 * __Zourlidou, S., Golze, J. and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

Changed the license of Traffic Regulator Ground-truth Information of the City of Hannover, Germany to CC-BY-NC-3.0 (previously )

Changed value of field source_metadata_modified to 2024-04-05T14:24:29.434933 in Traffic Regulator Ground-truth Information of the City of Hannover, Germany

              
    
          
          
        
        
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            n 2   "author": " S. Zourlidou", n 2   "author": "Zourlidou, Stefania",
            3   "author_email": "stefania.zourlidou@ikg.uni-hannover.de", 3   "author_email": "stefania.zourlidou@ikg.uni-hannover.de",
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            5   "doi": "10.25835/cqg0x1el", 6   "doi": "10.25835/cqg0x1el",
            6   "doi_date_published": "2022-08-01", 7   "doi_date_published": "2022-08-01",
            7   "doi_publisher": "LUIS", 8   "doi_publisher": "LUIS",
            8   "doi_status": "true", 9   "doi_status": "true",
            9   "domain": "https://data.uni-hannover.de", 10   "domain": "https://data.uni-hannover.de",
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            11     { 12   "givenName": "Stefania",
            12       "extra_author": " J. Golze", 
            13       "orcid": "" 
            14     }, 
            15     { 
            16       "extra_author": " M. Sester", 
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            25   "license_title": "", 18   "license_title": "CC-BY-NC-3.0",
            26   "maintainer": "Jens Golze", 19   "maintainer": "Jens Golze",
            27   "maintainer_email": "jens.golze@ikg.uni-hannover.de", 20   "maintainer_email": "jens.golze@ikg.uni-hannover.de",
            28   "metadata_created": "2023-01-12T13:14:15.021436", 21   "metadata_created": "2023-01-12T13:14:15.021436",
            n 29   "metadata_modified": "2024-02-16T14:20:58.601215", n 22   "metadata_modified": "2024-11-28T12:52:21.570634",
            30   "name": "luh-ground-truth-intersection-regulators-for-hannover", 23   "name": "luh-ground-truth-intersection-regulators-for-hannover",
            31   "notes": "This dataset contains the ground-truth intersection  24   "notes": "This dataset contains the ground-truth intersection 
            32 regulators for a majority of intersections of the city of Hannover,  25 regulators for a majority of intersections of the city of Hannover, 
            33 Germany.  The ground-truth information is used in order to apply  26 Germany.  The ground-truth information is used in order to apply 
            34 machine learning techniques on (car) GPS trajectory data in order to  27 machine learning techniques on (car) GPS trajectory data in order to 
            35 automatically detect the intersection  28 automatically detect the intersection 
            36 -1a67-4374-97c7-397b65dad394/download/hannover_rules_1.png)\r\n\r\nThe  29 -1a67-4374-97c7-397b65dad394/download/hannover_rules_1.png)\r\n\r\nThe 
            37 GPS trajectories related to specifically this dataset are (also)  30 GPS trajectories related to specifically this dataset are (also) 
            38 available under: https://doi.org/10.25835/9bidqxvl\r\n\r\n## Data  31 available under: https://doi.org/10.25835/9bidqxvl\r\n\r\n## Data 
            39 Acquisition\r\nThe ground-truth information are acquired by visiting  32 Acquisition\r\nThe ground-truth information are acquired by visiting 
            40 them on-site and apply manual labeling of each intersection arm  33 them on-site and apply manual labeling of each intersection arm 
            41 individually. Furthermore, satellite images and street-level images  34 individually. Furthermore, satellite images and street-level images 
            42 were considered but only on a minor degree as on-site labeling is  35 were considered but only on a minor degree as on-site labeling is 
            43 found to be more precise and up-to-date.\r\n\r\n## Related  36 found to be more precise and up-to-date.\r\n\r\n## Related 
            44 Publications:\r\n* __Zourlidou, S., Sester, M. and Hu, S. (2022):__  37 Publications:\r\n* __Zourlidou, S., Sester, M. and Hu, S. (2022):__ 
            45 Recognition of Intersection Traffic Regulations From Crowdsourced  38 Recognition of Intersection Traffic Regulations From Crowdsourced 
            46 Data. Preprints 2022, 2022070012. DOI:  39 Data. Preprints 2022, 2022070012. DOI: 
            n 47 10.20944/preprints202207.0012.v1\r\n\r\n* __Zourlidou, S., Golze, J.  n 40 https://doi.org/10.20944/preprints202207.0012.v1\r\n\r\n* __Zourlidou, 
            48 and Sester, M. (2022):__ Traffic Regulation Recognition using  41 S., Golze, J. and Sester, M. (2022):__ Traffic Regulation Recognition 
            49 Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience  42 using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE 
            50 Ser., 3, 22, 2022.  43 GIScience Ser., 3, 22, 2022. 
            51 https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H.,  44 https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H., 
            52 Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control  45 Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control 
            53 Recognition with an Attention Mechanism Using Speed-Profile and  46 Recognition with an Attention Mechanism Using Speed-Profile and 
            54 Satellite Imagery data. The International Archives of the  47 Satellite Imagery data. The International Archives of the 
            55 Photogrammetry, Remote Sensing and Spatial Information Sciences  48 Photogrammetry, Remote Sensing and Spatial Information Sciences 
            56 XLIII-B4-2022, S. 287\u201329.  49 XLIII-B4-2022, S. 287\u201329. 
            57 https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n*  50 https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n* 
            58 __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__  51 __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 
            59 Trajectory analysis at intersections for traffic rule identification.  52 Trajectory analysis at intersections for traffic rule identification. 
            60 Geo-spatial Information Science, 11(4):1-10.  53 Geo-spatial Information Science, 11(4):1-10. 
            61 https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H.,  54 https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H., 
            62 Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition  55 Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition 
            63 with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf.  56 with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 
            64 2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze,  57 2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, 
            65 J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection  58 J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection 
            66 Using GPS Trajectories. KN J. Cartogr. Geogr. Inf.  59 Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. 
            67 https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S.,  60 https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S., 
            68 Fischer, C. and Sester, M. (2019):__  Classification of street  61 Fischer, C. and Sester, M. (2019):__  Classification of street 
            69 junctions according to traffic regulators. In: _Kyriakidis, P.,  62 junctions according to traffic regulators. In: _Kyriakidis, P., 
            70 Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_.  63 Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 
            71 Accepted short papers and posters from the 22nd AGILE conference on  64 Accepted short papers and posters from the 22nd AGILE conference on 
            72 geo-information science. Cyprus University of Technology 17\u201320  65 geo-information science. Cyprus University of Technology 17\u201320 
            73 June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n*  66 June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n* 
            74 __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS  67 __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS 
            75 Trajectory Dataset of the Region of Hannover, Germany.  68 Trajectory Dataset of the Region of Hannover, Germany. 
            76 https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J.  69 https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J. 
            77 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth  70 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            78 Information for the Chicago Trajectory Dataset.  71 Information for the Chicago Trajectory Dataset. 
            79 https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J.  72 https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J. 
            80 and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic  73 and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic 
            81 Regulation Information of the Region of Edessa, Greece.  74 Regulation Information of the Region of Edessa, Greece. 
            82 https://doi.org/10.25835/v0mzwob3\r\n\r\n* __Zourlidou, S., Golze, J.  75 https://doi.org/10.25835/v0mzwob3\r\n\r\n* __Zourlidou, S., Golze, J. 
            83 and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories  76 and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories 
            84 for Traffic Rule Recognition (6 Junctions, Hannover, Germany).  77 for Traffic Rule Recognition (6 Junctions, Hannover, Germany). 
            85 https://doi.org/10.25835/0043786", 78 https://doi.org/10.25835/0043786",
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            275   "title": "Traffic Regulator Ground-truth Information of the City of  264   "title": "Traffic Regulator Ground-truth Information of the City of 
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            280   "version": "" 269   "version": ""
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