Digital Geography Lab, Department of Geosciences and Geography, University of Helsinki, Helsinki, 00014, Finland.
Helsinki Institute of Sustainability Science, University of Helsinki, Helsinki, 00014, Finland.
Conserv Biol. 2021 Apr;35(2):424-436. doi: 10.1111/cobi.13704. Epub 2021 Mar 22.
Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user-generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state-of-the-art computer-vision methods to studying human-nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human-nature interactions can be extracted from user-generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer-vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.
了解访客的活动和偏好对于管理保护区和规划保护策略至关重要。保护文化组学提倡在保护科学中使用用户生成的在线内容。地理标记的社交媒体内容是关于人类在自然中存在和活动的原位信息的独特来源。社交媒体平台上发布的照片是信息的有前途来源，但手动分析大量照片仍然很费力。我们研究了最先进的计算机视觉方法在研究人与自然相互作用中的应用。我们使用语义聚类、场景分类和对象检测来自动分析国内和国际游客在芬兰国家公园拍摄的照片。我们的结果表明，计算机视觉可以从用户生成的照片中提取人与自然的相互作用。不同的方法通过揭示与数据集水平、景观摄影同质性和人类活动相关的广泛视觉主题相互补充。地理标记的照片揭示了国家公园的明显区域特征（例如，对景观和活动的偏好），这在公园管理中可能很有用。国内和国际游客的照片内容不同，这表明他们的活动和偏好存在差异。从照片中自动提取的信息可以帮助识别不同访客群体的偏好，这可用于创建国家公园的简介，以进行保护营销，并支持依赖公众接受的保护策略。应进一步探索计算机视觉方法在保护文化组学中的自动照片内容分析中的应用，特别是与社交媒体平台上可用的丰富元数据相结合。
