High-quality roads are the scaffolding for prosperous and healthy societies, and accordingly garner huge investments from governments every year. However, current techniques to monitor those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. In this work, we develop a model for monitoring the quality of road infrastructure using satellite imagery, enabling much larger scale and much lower costs than are achievable with current methods. For this task, we harness two trends: the increasing availability of high-resolution, often-updated satellite imagery, and substantial improvement in accuracy and performance of neural network-based methods for executing computer vision tasks. In this study, we train a model for intercity road quality prediction using a unique dataset of road quality measurement labels (57 roads, total length is 7000km) throughout the Republic of Kenya combined with corresponding 50cm resolution satellite imagery. Using a variety of neural network architectures, we create and evaluate regression models for predicting road quality. Our results show a best-case R2 value of 0.79 for the regression problem using a standard train-test split and an R2 value of 0.35 for the substantially harder held-out regression problem which has the added potential to generalize more readily to other contexts. We further demonstrate the potential of our measurement technique with a large-scale case study (for 322 towns throughout Kenya) that shows a positive relationship between incoming road quality and nighttime illumination, a common proxy measurement for local economic activity. These results indicate the possibility to measure road quality at an unprecedented scale, providing insight into the contribution of high-quality roads to many societal development indicators.

Societal-scale data is playing an increasingly prominent role in social science research; examples from research on geopolitical events include questions on how emergency events impact the diffusion of information or how new policies change patterns of social interaction. Such research often draws critical inferences from observing how an exogenous event changes meaningful metrics like network degree or network entropy. However, as we show in this work, standard estimation methodologies make systematically incorrect inferences when the event also changes the sparsity of the data.

Abstract Cell-site simulators, also known as IMSI-catchers and stingrays, are used around the world by governments and criminals to track and eavesdrop on cell phones. Despite extensive public debate surrounding their use, few hard facts about them are available. For example, the richest sources of information on U.S. government cell-site simulator usage are from anonymous leaks, public records requests, and court proceedings. This lack of concrete information and the difficulty of independently obtaining such information hampers the public discussion. To address this deficiency, we build, deploy, and evaluate SeaGlass, a city-wide cellsite simulator detection network. SeaGlass consists of sensors that measure and upload data on the cellular environment to find the signatures of portable cell-site simulators. SeaGlass sensors are designed to be robust, low-maintenance, and deployable in vehicles for long durations. The data they generate is used to learn a city’s network properties to find anomalies consistent with cell-site simulators. We installed SeaGlass sensors into 15 ridesharing vehicles across two cities, collecting two months of data in each city. Using this data, we evaluate the system and show how SeaGlass can be used to detect signatures of portable cell-site simulators. Finally, we evaluate our signature detection methods and discuss anomalies discovered in the data.