Road networks are among the most essential components of a country's infrastructure. By facilitating the movement and exchange of goods, people, and ideas, they support economic and cultural activity both within and across borders. Up-to-date mapping of the the geographical distribution of roads and their quality is essential in high-impact applications ranging from land use planning to wilderness conservation. Mapping presents a particularly pressing challenge in developing countries, where documentation is poor and disproportionate amounts of road construction are expected to occur in the coming decades. We present a new crowd-sourced approach capable of assessing road quality and identify key challenges and opportunities in the transferability of deep learning based methods across domains.
@article{arxiv.2012.00179,
title = {Crowd-Sourced Road Quality Mapping in the Developing World},
author = {Benjamin Choi and John Kamalu},
journal= {arXiv preprint arXiv:2012.00179},
year = {2020}
}
Comments
Presented at NeurIPS 2020 Workshop on Machine Learning for the Developing World