Towards fine-grained road maps extraction using sentinel-2 imagery
| dc.contributor.author | Ayala Lauroba, Christian | |
| dc.contributor.author | Aranda, Carlos | |
| dc.contributor.author | Galar Idoate, Mikel | |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.funder | Gobierno de Navarra / Nafarroako Gobernua | es |
| dc.date.accessioned | 2022-01-17T12:48:00Z | |
| dc.date.available | 2022-01-17T12:48:00Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Nowadays, it is highly important to keep road maps up-to-date since a great deal of services rely on them. However, to date, these labours have demanded a great deal of human attention due to their complexity. In the last decade, promising attempts have been carried out to fully-automatize the extraction of road networks from remote sensing imagery. Nevertheless, the vast majority of methods rely on aerial imagery (< 1 m), whose costs are not yet affordable for maintaining up-to-date maps. This work proves that it is also possible to accurately detect roads using high resolution satellite imagery (10 m). Accordingly, we have relied on Sentinel-2 imagery considering its freely availability and the higher revisit times compared to aerial imagery. It must be taken into account that the lack of spatial resolution of this sensor drastically increases the difficulty of the road detection task, since the feasibility to detect a road depends on its width, which can reach sub-pixel size in Sentinel-2 imagery. For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed. As a result, fine-grained road maps at 2.5 m are generated from Sentinel-2 imagery. | en |
| dc.description.sponsorship | Christian Ayala was partially supported by the Goverment of Navarra under the industrial PhD program 2020 reference 0011-1408-2020-000008. Mikel Galar was partially supported by Tracasa Instrumental S.L. under projects OTRI 2018-901-073, OTRI 2019-901-091 and OTRI 2020-901-050. | en |
| dc.format.extent | 6 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.5194/isprs-annals-V-3-2021-9-2021 | |
| dc.identifier.issn | 2196-6346 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41806 | |
| dc.language.iso | eng | en |
| dc.publisher | Copernicus | |
| dc.relation.ispartof | Isprs Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5 (3), 9-14 | |
| dc.relation.publisherversion | http://doi.org/10.5194/isprs-annals-V-3-2021-9-2021 | |
| dc.rights | © Author(s) 2021. Creative Commons Attribution 4.0 International License | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Convolutional neural networks | en |
| dc.subject | Deep learning | en |
| dc.subject | Remote sensing | en |
| dc.subject | Road network extraction | en |
| dc.subject | Sentinel-2 | en |
| dc.title | Towards fine-grained road maps extraction using sentinel-2 imagery | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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