Road Mapping in Low Data Environments with OpenStreetMap
- URL: http://arxiv.org/abs/2006.07993v1
- Date: Sun, 14 Jun 2020 19:39:57 GMT
- Title: Road Mapping in Low Data Environments with OpenStreetMap
- Authors: John Kamalu, Benjamin Choi
- Abstract summary: A comprehensive, up-to-date mapping of the geographical distribution of roads has the potential to act as an indicator for broader economic development.
This work investigates the viability of high resolution satellite imagery and crowd-sourced resources like OpenStreetMap in the construction of such a mapping.
- Score: 0.3162999570707049
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Roads are among the most essential components of any country's
infrastructure. By facilitating the movement and exchange of people, ideas, and
goods, they support economic and cultural activity both within and across local
and international borders. A comprehensive, up-to-date mapping of the
geographical distribution of roads and their quality thus has the potential to
act as an indicator for broader economic development. Such an indicator has a
variety of high-impact applications, particularly in the planning of rural
development projects where up-to-date infrastructure information is not
available. This work investigates the viability of high resolution satellite
imagery and crowd-sourced resources like OpenStreetMap in the construction of
such a mapping. We experiment with state-of-the-art deep learning methods to
explore the utility of OpenStreetMap data in road classification and
segmentation tasks. We also compare the performance of models in different mask
occlusion scenarios as well as out-of-country domains. Our comparison raises
important pitfalls to consider in image-based infrastructure classification
tasks, and shows the need for local training data specific to regions of
interest for reliable performance.
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