Roads are critically important infrastructure to societal and economic
development, with huge investments made by governments every year. However,
methods for monitoring 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. For this task, we harness two trends: the increasing
availability of high-resolution, often-updated satellite imagery, and the
enormous improvement in speed and accuracy of convolutional neural
network-based methods for performing computer vision tasks. We employ a unique
dataset of road quality information on 7000km of roads in Kenya combined with
50cm resolution satellite imagery. We create models for a binary classification
task as well as a comprehensive 5-category classification task, with accuracy
scores of 88 and 73 percent respectively. We also provide evidence of the
robustness of our methods with challenging held-out scenarios, though we note
some improvement is still required for confident analysis of a never before
seen road. We believe these results are well-positioned to have substantial
impact on a broad set of transport applications.

利用卫星图像、卷积神经网络等技术，开发了一种监测道路基础设施的模型，并取得了较为优异的分类效果，该方法可在交通运输等众多领域具有广泛应用前景。