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Predicting road quality using high resolution satellite imagery: A transfer learning approach

Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity—or complete lack—of accurate information regarding existing road...

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Detalles Bibliográficos
Autores principales: Brewer, Ethan, Lin, Jason, Kemper, Peter, Hennin, John, Runfola, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270213/
https://www.ncbi.nlm.nih.gov/pubmed/34242250
http://dx.doi.org/10.1371/journal.pone.0253370
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author Brewer, Ethan
Lin, Jason
Kemper, Peter
Hennin, John
Runfola, Dan
author_facet Brewer, Ethan
Lin, Jason
Kemper, Peter
Hennin, John
Runfola, Dan
author_sort Brewer, Ethan
collection PubMed
description Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity—or complete lack—of accurate information regarding existing road infrastructure, challenging the effective identification of where investments should be made. Previous literature has focused on overcoming this gap through the use of satellite imagery to detect and map roads. In this piece, we extend this literature by leveraging satellite imagery to estimate road quality and concomitant information about travel speed. We adopt a transfer learning approach in which a convolutional neural network architecture is first trained on data collected in the United States (where data is readily available), and then “fine-tuned” on an independent, smaller dataset collected from Nigeria. We test and compare eight different convolutional neural network architectures using a dataset of 53,686 images of 2,400 kilometers of roads in the United States, in which each road segment is measured as “low”, “middle”, or “high” quality using an open, cellphone-based measuring platform. Using satellite imagery to estimate these classes, we achieve an accuracy of 80.0%, with 99.4% of predictions falling within the actual or an adjacent class. The highest performing base model was applied to a preliminary case study in Nigeria, using a dataset of 1,000 images of paved and unpaved roads. By tailoring our US-model on the basis of this Nigeria-specific data, we were able to achieve an accuracy of 94.0% in predicting the quality of Nigerian roads. A continuous case estimate also showed the ability, on average, to predict road quality to within 0.32 on a 0 to 3 scale (with higher values indicating higher levels of quality).
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spelling pubmed-82702132021-07-21 Predicting road quality using high resolution satellite imagery: A transfer learning approach Brewer, Ethan Lin, Jason Kemper, Peter Hennin, John Runfola, Dan PLoS One Research Article Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity—or complete lack—of accurate information regarding existing road infrastructure, challenging the effective identification of where investments should be made. Previous literature has focused on overcoming this gap through the use of satellite imagery to detect and map roads. In this piece, we extend this literature by leveraging satellite imagery to estimate road quality and concomitant information about travel speed. We adopt a transfer learning approach in which a convolutional neural network architecture is first trained on data collected in the United States (where data is readily available), and then “fine-tuned” on an independent, smaller dataset collected from Nigeria. We test and compare eight different convolutional neural network architectures using a dataset of 53,686 images of 2,400 kilometers of roads in the United States, in which each road segment is measured as “low”, “middle”, or “high” quality using an open, cellphone-based measuring platform. Using satellite imagery to estimate these classes, we achieve an accuracy of 80.0%, with 99.4% of predictions falling within the actual or an adjacent class. The highest performing base model was applied to a preliminary case study in Nigeria, using a dataset of 1,000 images of paved and unpaved roads. By tailoring our US-model on the basis of this Nigeria-specific data, we were able to achieve an accuracy of 94.0% in predicting the quality of Nigerian roads. A continuous case estimate also showed the ability, on average, to predict road quality to within 0.32 on a 0 to 3 scale (with higher values indicating higher levels of quality). Public Library of Science 2021-07-09 /pmc/articles/PMC8270213/ /pubmed/34242250 http://dx.doi.org/10.1371/journal.pone.0253370 Text en © 2021 Brewer et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Brewer, Ethan
Lin, Jason
Kemper, Peter
Hennin, John
Runfola, Dan
Predicting road quality using high resolution satellite imagery: A transfer learning approach
title Predicting road quality using high resolution satellite imagery: A transfer learning approach
title_full Predicting road quality using high resolution satellite imagery: A transfer learning approach
title_fullStr Predicting road quality using high resolution satellite imagery: A transfer learning approach
title_full_unstemmed Predicting road quality using high resolution satellite imagery: A transfer learning approach
title_short Predicting road quality using high resolution satellite imagery: A transfer learning approach
title_sort predicting road quality using high resolution satellite imagery: a transfer learning approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270213/
https://www.ncbi.nlm.nih.gov/pubmed/34242250
http://dx.doi.org/10.1371/journal.pone.0253370
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