The unprecedented development of the internet has compelled a growing number of tourists to share their photographs on social media. These images convey valuable memories and points of interest. As photography and content sharing have become commonplace among visitors, pictorial digital footprints represent a prevalent topic in tourism research. Studies on tourists’ movement trajectories hold great importance for destination management, marketing, and services. Flickr is a popular source in photo-based tourism research given the digital footprints embedded in photos’ metadata; however, the site’s bottlenecks (e.g. declining user activity, overly professional photographs) raise concerns. Scholars have instead gradually shifted their attention to emerging photo platforms such as Instagram—yet these pictures do not contain geographical information. Taking Beijing as a focal location, we introduce an approach in which landmark recognition complements the geographical cues in Instagram photos. Instagram check-in data and data identified through landmark recognition are validated. Ultimately, the recognized landmark information appears highly correlated with check-in data. This study demonstrates the feasibility of landmark recognition for extracting tourists’ footprints from ordinary content in user-generated photos. Findings also confirm that many photos from general social media platforms can serve as alternative and representative data sources in photo-based tourism research.
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