StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection
The recent advancements in the field of deep learning have fundamentally altered the manner in which certain challenges and problems are addressed. One area that stands to greatly benefit from such innovations is the realm of urban planning, where the utilization of these tools can facilitate the au...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923001609 |
| _version_ | 1797798084926767104 |
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| author | Sotirios Moschos Polychronis Charitidis Stavros Doropoulos Anastasios Avramis Stavros Vologiannidis |
| author_facet | Sotirios Moschos Polychronis Charitidis Stavros Doropoulos Anastasios Avramis Stavros Vologiannidis |
| author_sort | Sotirios Moschos |
| collection | DOAJ |
| description | The recent advancements in the field of deep learning have fundamentally altered the manner in which certain challenges and problems are addressed. One area that stands to greatly benefit from such innovations is the realm of urban planning, where the utilization of these tools can facilitate the automatic detection of landscape objects in a given area. However, it must be noted that these data-driven methodologies necessitate significant amounts of training data to attain desired results. This challenge can be mitigated through the application of transfer learning techniques, which reduce the amount of required data and permit the customization of these models through fine-tuning. The present study presents street-level imagery, which can be utilized for fine-tuning and deployment of custom object detectors in urban environments.The dataset comprises 763 images, each accompanied by bounding box annotations for five landscape object classes, including trees, waste bins, recycling bins, shop storefronts, and lighting poles. Furthermore, the dataset includes sequential frame data obtained from a camera mounted on a vehicle, capturing a total of three hours of driving, encompassing various regions within the city center of Thessaloniki. |
| first_indexed | 2024-03-13T03:58:09Z |
| format | Article |
| id | doaj.art-8aaf9da744c14573b61f739f09ee42ed |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-13T03:58:09Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-8aaf9da744c14573b61f739f09ee42ed2023-06-22T05:03:22ZengElsevierData in Brief2352-34092023-06-0148109042StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detectionSotirios Moschos0Polychronis Charitidis1Stavros Doropoulos2Anastasios Avramis3Stavros Vologiannidis4DataScouting, 30 Vakchou Street, 54629 Thessaloniki, Greece; Corresponding author.DataScouting, 30 Vakchou Street, 54629 Thessaloniki, GreeceDataScouting, 30 Vakchou Street, 54629 Thessaloniki, GreeceDataScouting, 30 Vakchou Street, 54629 Thessaloniki, GreeceDepartment of Computer, Informatics and Telecommunications Engineering, International Hellenic University, Terma Magnisias, 62124 Serres, GreeceThe recent advancements in the field of deep learning have fundamentally altered the manner in which certain challenges and problems are addressed. One area that stands to greatly benefit from such innovations is the realm of urban planning, where the utilization of these tools can facilitate the automatic detection of landscape objects in a given area. However, it must be noted that these data-driven methodologies necessitate significant amounts of training data to attain desired results. This challenge can be mitigated through the application of transfer learning techniques, which reduce the amount of required data and permit the customization of these models through fine-tuning. The present study presents street-level imagery, which can be utilized for fine-tuning and deployment of custom object detectors in urban environments.The dataset comprises 763 images, each accompanied by bounding box annotations for five landscape object classes, including trees, waste bins, recycling bins, shop storefronts, and lighting poles. Furthermore, the dataset includes sequential frame data obtained from a camera mounted on a vehicle, capturing a total of three hours of driving, encompassing various regions within the city center of Thessaloniki.http://www.sciencedirect.com/science/article/pii/S2352340923001609Street DataUrban objectsObject detectionDeep learning |
| spellingShingle | Sotirios Moschos Polychronis Charitidis Stavros Doropoulos Anastasios Avramis Stavros Vologiannidis StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection Data in Brief Street Data Urban objects Object detection Deep learning |
| title | StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection |
| title_full | StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection |
| title_fullStr | StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection |
| title_full_unstemmed | StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection |
| title_short | StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection |
| title_sort | streetscouting dataset a street level image dataset for finetuning and applying custom object detectors for urban feature detection |
| topic | Street Data Urban objects Object detection Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923001609 |
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