Abstract
Researchers in the social sciences are interested in the consequences of institutions, increasingly on a global scale. Institutions that may be negotiated between states can have consequences at a microlevel, as local populations adjust their expectations and ultimately even their behavior to take institutional rules into account. However, large-scale fine-grained analyses that test for the complex evidence of such institutions locally are rare. This article focuses on a key institution: International borders. Using computer vision techniques, we show that it is possible to produce a geographically specific, validated, and replicable way to characterize border legibility, by which we mean the ability to visually detect the presence of an international border in physical space. We develop and compare computer vision techniques to automatically estimate legibility scores for 627,656 imagery tiles from virtually every border in the world. We evaluate statistical and data-driven computer vision methods, finding that fine-tuning pretrained visual recognition models on a small set of human judgments allows us to produce local legibility scores globally that align well with human notions of legibility. Finally, we interpret these scores as useful approximations of states' border orientations, a concept that prior literature has used to capture the visible investments states make in border areas to maintain jurisdictional authority territorially. We validate our measurement strategy using both human judgments and five nomological validation indicators.