Street Smarts: Measuring Intercity Road Quality Using Deep Learning On Satellite Imagery

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 R-2 value of 0.79 for the regression problem using a standard train-test split and an R-2 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 or 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.