Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs
Recent technological advancements in geomatics and mobile sensing have led to various urban big data, such as Tencent street view (TSV) photographs; yet, the urban objects in the big dataset have hitherto been inadequately exploited. This paper aims to propose a pedestrian analytics approach named v...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/8/561 |
| _version_ | 1797523655406649344 |
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| author | Fan Xue Xiao Li Weisheng Lu Christopher J. Webster Zhe Chen Lvwen Lin |
| author_facet | Fan Xue Xiao Li Weisheng Lu Christopher J. Webster Zhe Chen Lvwen Lin |
| author_sort | Fan Xue |
| collection | DOAJ |
| description | Recent technological advancements in geomatics and mobile sensing have led to various urban big data, such as Tencent street view (TSV) photographs; yet, the urban objects in the big dataset have hitherto been inadequately exploited. This paper aims to propose a pedestrian analytics approach named vectors of uncountable and countable objects for clustering and analysis (VUCCA) for processing 530,000 TSV photographs of Hong Kong Island. First, VUCCA transductively adopts two pre-trained deep models to TSV photographs for extracting pedestrians and surrounding pixels into generalizable semantic vectors of features, including uncountable objects such as vegetation, sky, paved pedestrian path, and guardrail and countable objects such as cars, trucks, pedestrians, city animals, and traffic lights. Then, the extracted pedestrians are semantically clustered using the vectors, e.g., for understanding where they usually stand. Third, pedestrians are semantically indexed using relations and activities (e.g., walking behind a guardrail, road-crossing, carrying a backpack, or walking a pet) for queries of unstructured photographic instances or natural language clauses. The experiment results showed that the pedestrians detected in the TSV photographs were successfully clustered into meaningful groups and indexed by the semantic vectors. The presented VUCCA can enrich eye-level urban features into computational semantic vectors for pedestrians to enable smart city research in urban geography, urban planning, real estate, transportation, conservation, and other disciplines. |
| first_indexed | 2024-03-10T08:46:08Z |
| format | Article |
| id | doaj.art-ac50ce08497d43a3bce8dfedc802bfae |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T08:46:08Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-ac50ce08497d43a3bce8dfedc802bfae2023-11-22T07:53:43ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-08-0110856110.3390/ijgi10080561Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View PhotographsFan Xue0Xiao Li1Weisheng Lu2Christopher J. Webster3Zhe Chen4Lvwen Lin5Department of Real Estate and Construction, The University of Hong Kong, Hong Kong 999077, ChinaDepartment of Real Estate and Construction, The University of Hong Kong, Hong Kong 999077, ChinaDepartment of Real Estate and Construction, The University of Hong Kong, Hong Kong 999077, ChinaFaculty of Architecture, The University of Hong Kong, Hong Kong 999077, ChinaDepartment of Real Estate and Construction, The University of Hong Kong, Hong Kong 999077, ChinaDepartment of Business Ecosystems, JD Technology, 67 South Zhongxing Road, Panyu District, Guangzhou 511495, ChinaRecent technological advancements in geomatics and mobile sensing have led to various urban big data, such as Tencent street view (TSV) photographs; yet, the urban objects in the big dataset have hitherto been inadequately exploited. This paper aims to propose a pedestrian analytics approach named vectors of uncountable and countable objects for clustering and analysis (VUCCA) for processing 530,000 TSV photographs of Hong Kong Island. First, VUCCA transductively adopts two pre-trained deep models to TSV photographs for extracting pedestrians and surrounding pixels into generalizable semantic vectors of features, including uncountable objects such as vegetation, sky, paved pedestrian path, and guardrail and countable objects such as cars, trucks, pedestrians, city animals, and traffic lights. Then, the extracted pedestrians are semantically clustered using the vectors, e.g., for understanding where they usually stand. Third, pedestrians are semantically indexed using relations and activities (e.g., walking behind a guardrail, road-crossing, carrying a backpack, or walking a pet) for queries of unstructured photographic instances or natural language clauses. The experiment results showed that the pedestrians detected in the TSV photographs were successfully clustered into meaningful groups and indexed by the semantic vectors. The presented VUCCA can enrich eye-level urban features into computational semantic vectors for pedestrians to enable smart city research in urban geography, urban planning, real estate, transportation, conservation, and other disciplines.https://www.mdpi.com/2220-9964/10/8/561urban informaticsbig datapedestrian activitystreetscapeTencent street view (TSV)deep learning |
| spellingShingle | Fan Xue Xiao Li Weisheng Lu Christopher J. Webster Zhe Chen Lvwen Lin Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs ISPRS International Journal of Geo-Information urban informatics big data pedestrian activity streetscape Tencent street view (TSV) deep learning |
| title | Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs |
| title_full | Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs |
| title_fullStr | Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs |
| title_full_unstemmed | Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs |
| title_short | Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs |
| title_sort | big data driven pedestrian analytics unsupervised clustering and relational query based on tencent street view photographs |
| topic | urban informatics big data pedestrian activity streetscape Tencent street view (TSV) deep learning |
| url | https://www.mdpi.com/2220-9964/10/8/561 |
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