Quantifying and mapping cultural ecosystem services is complex because of their intangibility. Data from social media, such as geo-tagged photographs, have been proposed for mapping cultural use or appreciation of ecosystems. However, manual content analysis and classification of large numbers of photographs is time consuming. This study develops a novel method for automating content analysis of social media photographs for ecosystem services assessment. The approach applies an online machine learning algorithm – Google Cloud Vision – to analyse over 20,000 photographs from Singapore, and uses hierarchical clustering to group these photographs. The accuracy of the classification was assessed by comparison with manual classification. Over 20% of photographs were taken of nature, being of animals or plants. The distribution of nature photographs was concentrated around particular natural attractions, and nature photographs were more likely to occur in parks and areas of high vegetation cover. The approach developed for clustering photographs was accurate and saved approximately 170 h of manual work. The method provides an indicator of cultural ecosystem services that can be applied rapidly over large areas. Automated assessment and mapping of cultural ecosystem services could be used to inform urban planning.

由于文化生态系统服务的无形性，因此对其进行量化和绘图很复杂。已经提出了来自社会媒体的数据，例如带有地理标签的照片，用于绘制文化用途或生态系统欣赏的地图。但是，手动内容分析和大量照片的分类非常耗时。这项研究开发了一种新颖的方法，用于自动化社交媒体照片的内容分析，以进行生态系统服务评估。该方法采用在线机器学习算法-Google Cloud Vision-分析来自新加坡的20,000多张照片，并使用层次聚类对这些照片进行分组。通过与手动分类进行比较来评估分类的准确性。超过20％的照片是自然界的动物或植物。大自然照片的分布集中在特定的自然景点周围，大自然照片更可能出现在公园和植被覆盖率较高的地区。为照片聚类而开发的方法是准确的，并且节省了大约170个小时的手工工作。该方法提供了可在大范围快速应用的文化生态系统服务的指标。文化生态系统服务的自动评估和绘图可用于为城市规划提供信息。该方法提供了可在大范围快速应用的文化生态系统服务的指标。文化生态系统服务的自动评估和绘图可用于为城市规划提供信息。该方法提供了可在大范围快速应用的文化生态系统服务的指标。文化生态系统服务的自动评估和绘图可用于为城市规划提供信息。