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.

中文翻译：

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利用社交媒体照片和计算机视觉探索国家公园中的人与自然的互动

了解游客的活动和喜好对于管理保护区和规划保护战略至关重要。保护文化学促进了用户生成的在线内容在保护科学中的使用。带有地理标签的社交媒体内容是有关人类活动和自然活动的原位信息的唯一来源。在社交媒体平台上发布的照片​​是有希望的信息来源，但是手动分析大量照片仍然很费力。我们研究了最新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外游客在芬兰国家公园拍摄的照片。我们的结果表明，可以使用计算机视觉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，景观光生性和人类活动有关的广泛视觉主题而相互补充。带有地理标签的照片揭示了国家公园的不同区域概况（例如，对风景和活动的偏好），这在公园管理中可能很有用。国内外访客的摄影内容有所不同，这表明活动和喜好有所不同。从照片中自动提取的信息可以帮助识别不同游客群体之间的偏好，这些信息可以用于创建国家公园的概况，以进行保护性营销并支持依赖公众接受的保护性策略。