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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 characterizeborder 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. 

Key aspects of international policy, such as those pertaining to migration and trade, manifest in the physical world at international political borders; for this reason, borders are of interest to political science studying the impacts and implications of these policies. While some prior efforts have worked to characterize features of borders us- ing trained human coders and crowdsourcing, these are limited in scale by the need for manual annotations. In this paper, we present a new task, dataset, and baseline approaches for estimating the legibility of international political borders automatically and on a global scale. Our contributions are to (1) define the border legibility estimation task; (2) collect a dataset of overhead (aerial) imagery for the entire world’s international borders, (3) propose several classical and deep-learning-based approaches to establish a baseline for the task, and (4) evaluate our algorithms against a validation dataset of crowdsourced legibility com- parisons. Our results on this challenging task confirm that while low-level features can often explain border legibility, mid- and high-level features are also important. Finally, we show preliminary results of a global analysis of legibility, confirming some of the political and geographic influences of legibility.