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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Autores principales:	Ethan Brewer, Jason Lin, Peter Kemper, John Hennin, Dan Runfola
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3049a5bdc60d4bcda31227041cbc08dd
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spelling	oai:doaj.org-article:3049a5bdc60d4bcda31227041cbc08dd2021-12-02T20:05:07ZPredicting road quality using high resolution satellite imagery: A transfer learning approach.1932-620310.1371/journal.pone.0253370https://doaj.org/article/3049a5bdc60d4bcda31227041cbc08dd2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253370https://doaj.org/toc/1932-6203Recognizing 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).Ethan BrewerJason LinPeter KemperJohn HenninDan RunfolaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253370 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ethan Brewer Jason Lin Peter Kemper John Hennin Dan Runfola Predicting road quality using high resolution satellite imagery: A transfer learning approach.
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).
format	article
author	Ethan Brewer Jason Lin Peter Kemper John Hennin Dan Runfola
author_facet	Ethan Brewer Jason Lin Peter Kemper John Hennin Dan Runfola
author_sort	Ethan Brewer
title	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_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_sort	predicting road quality using high resolution satellite imagery: a transfer learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3049a5bdc60d4bcda31227041cbc08dd
work_keys_str_mv	AT ethanbrewer predictingroadqualityusinghighresolutionsatelliteimageryatransferlearningapproach AT jasonlin predictingroadqualityusinghighresolutionsatelliteimageryatransferlearningapproach AT peterkemper predictingroadqualityusinghighresolutionsatelliteimageryatransferlearningapproach AT johnhennin predictingroadqualityusinghighresolutionsatelliteimageryatransferlearningapproach AT danrunfola predictingroadqualityusinghighresolutionsatelliteimageryatransferlearningapproach
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Predicting road quality using high resolution satellite imagery: A transfer learning approach.

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