Water View Imagery: Perception and evaluation of urban waterscapes worldwide
Gathering knowledge about physical settings and visual information of places has long been of interest to a wide variety of fields as they affect the experience of observers. Previous studies have relied on on-site surveys, low-throughput methods, and limited data sources, which especially hinder an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X22010883 |
| _version_ | 1811209148938846208 |
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| author | Junjie Luo Tianhong Zhao Lei Cao Filip Biljecki |
| author_facet | Junjie Luo Tianhong Zhao Lei Cao Filip Biljecki |
| author_sort | Junjie Luo |
| collection | DOAJ |
| description | Gathering knowledge about physical settings and visual information of places has long been of interest to a wide variety of fields as they affect the experience of observers. Previous studies have relied on on-site surveys, low-throughput methods, and limited data sources, which especially hinder analyzing waterscape features. Thus, detecting the relationships between the human perception results of large-scale urban water areas and the waterfront features at high spatial resolutions remains challenging, and worldwide studies have not been conducted. We investigate an alternative: a data-driven waterscapes evaluation approach based on computer vision (CV) to analyze water view imagery (WVI) in 16 cities around the world and measure how people perceive scenes using virtual reality (VR). We bring attention to WVI – the counterpart of street view imagery (SVI) on water bodies, which is readily available for many cities thanks to the usual SVI services, but has been entirely overlooked in research hitherto. Specifically, a deep learning model, which has been trained with 500 segmented water-level photos, was developed to analyze them, achieving the mean pixel accuracy (MPA) of 94%, which advances state of the art. These panoramic images have been assessed through a virtual experience survey in which 60 participants indicated their perceptions across multiple dimensions. Afterwards, a series of statistical analyses were conducted to determine the visual indicators that drive perceptions, and the relationship between the people’s subjective visual perceptions and objective waterscape environment as seen by machines has been established. The results take researchers and watercourse planners one step toward understanding the interactions of the perceptions and semantics of water areas globally. The large-scale dataset we produced in this research has been released openly as the first such instance of open segmented water view imagery, and it is intended to support future studies. |
| first_indexed | 2024-04-12T04:34:42Z |
| format | Article |
| id | doaj.art-7621864a69584b06b5caeb766c5817c8 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-12T04:34:42Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-7621864a69584b06b5caeb766c5817c82022-12-22T03:47:50ZengElsevierEcological Indicators1470-160X2022-12-01145109615Water View Imagery: Perception and evaluation of urban waterscapes worldwideJunjie Luo0Tianhong Zhao1Lei Cao2Filip Biljecki3Department of Landscape Architecture, Tianjin University, Tianjin, China; Department of Architecture, National University of Singapore, SingaporeDepartment of Architecture, National University of Singapore, Singapore; School of Architecture and Urban Planning, Shenzhen University, ChinaDepartment of Landscape Architecture, Tianjin University, Tianjin, ChinaDepartment of Architecture, National University of Singapore, Singapore; Department of Real Estate, National University of Singapore, Singapore; Corresponding author at: Department of Architecture, National University of Singapore, Singapore.Gathering knowledge about physical settings and visual information of places has long been of interest to a wide variety of fields as they affect the experience of observers. Previous studies have relied on on-site surveys, low-throughput methods, and limited data sources, which especially hinder analyzing waterscape features. Thus, detecting the relationships between the human perception results of large-scale urban water areas and the waterfront features at high spatial resolutions remains challenging, and worldwide studies have not been conducted. We investigate an alternative: a data-driven waterscapes evaluation approach based on computer vision (CV) to analyze water view imagery (WVI) in 16 cities around the world and measure how people perceive scenes using virtual reality (VR). We bring attention to WVI – the counterpart of street view imagery (SVI) on water bodies, which is readily available for many cities thanks to the usual SVI services, but has been entirely overlooked in research hitherto. Specifically, a deep learning model, which has been trained with 500 segmented water-level photos, was developed to analyze them, achieving the mean pixel accuracy (MPA) of 94%, which advances state of the art. These panoramic images have been assessed through a virtual experience survey in which 60 participants indicated their perceptions across multiple dimensions. Afterwards, a series of statistical analyses were conducted to determine the visual indicators that drive perceptions, and the relationship between the people’s subjective visual perceptions and objective waterscape environment as seen by machines has been established. The results take researchers and watercourse planners one step toward understanding the interactions of the perceptions and semantics of water areas globally. The large-scale dataset we produced in this research has been released openly as the first such instance of open segmented water view imagery, and it is intended to support future studies.http://www.sciencedirect.com/science/article/pii/S1470160X22010883GeoAISemantic segmentationVR perceptionCrowdsourcingMapillaryGoogle Street View |
| spellingShingle | Junjie Luo Tianhong Zhao Lei Cao Filip Biljecki Water View Imagery: Perception and evaluation of urban waterscapes worldwide Ecological Indicators GeoAI Semantic segmentation VR perception Crowdsourcing Mapillary Google Street View |
| title | Water View Imagery: Perception and evaluation of urban waterscapes worldwide |
| title_full | Water View Imagery: Perception and evaluation of urban waterscapes worldwide |
| title_fullStr | Water View Imagery: Perception and evaluation of urban waterscapes worldwide |
| title_full_unstemmed | Water View Imagery: Perception and evaluation of urban waterscapes worldwide |
| title_short | Water View Imagery: Perception and evaluation of urban waterscapes worldwide |
| title_sort | water view imagery perception and evaluation of urban waterscapes worldwide |
| topic | GeoAI Semantic segmentation VR perception Crowdsourcing Mapillary Google Street View |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X22010883 |
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