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      https://www.riss.kr/link?id=A109488667

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      다국어 초록 (Multilingual Abstract)

      Cultural ecosystem services(CES) are intangible benefits that people obtain from nature such as scenic beauty, recreational activities, and non-use values. However, due to their characteristics, it has been difficult to quantify and map the services compared to other ecosystem services including provisioning or regulating services. Recent technical development has offered an alternative indicator to reflect where people go and what the attractive features there could be using crowd-sourced photos. However, this approach has been also limited to the meta information the contents have been manually labeled. In this study, we propose an alternative approach in analyzing the contents of large volumes of crowd-sourced photographs. This method applies machine-learning and network analysis to group the photos into two groups: CES-related, non-CES related, so we can further consider only those that are related to CES and minimize bias in the data. We applied this approach in the greater Seoul area and found that the social media photos were efficiently processed using published computer vision models which enabled us to differentiate themes. The non-CES photos were abundant esp. in primary and secondary center of the city. The presented approach has shown its potential for CES mapping especially in the region where CES-related and non-CES related photos are mixed up.
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      Cultural ecosystem services(CES) are intangible benefits that people obtain from nature such as scenic beauty, recreational activities, and non-use values. However, due to their characteristics, it has been difficult to quantify and m...

      Cultural ecosystem services(CES) are intangible benefits that people obtain from nature such as scenic beauty, recreational activities, and non-use values. However, due to their characteristics, it has been difficult to quantify and map the services compared to other ecosystem services including provisioning or regulating services. Recent technical development has offered an alternative indicator to reflect where people go and what the attractive features there could be using crowd-sourced photos. However, this approach has been also limited to the meta information the contents have been manually labeled. In this study, we propose an alternative approach in analyzing the contents of large volumes of crowd-sourced photographs. This method applies machine-learning and network analysis to group the photos into two groups: CES-related, non-CES related, so we can further consider only those that are related to CES and minimize bias in the data. We applied this approach in the greater Seoul area and found that the social media photos were efficiently processed using published computer vision models which enabled us to differentiate themes. The non-CES photos were abundant esp. in primary and secondary center of the city. The presented approach has shown its potential for CES mapping especially in the region where CES-related and non-CES related photos are mixed up.

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