A deep learning approach to an enhanced building footprint and road detection in high-resolution satellite imagery
| dc.contributor.author | Ayala Lauroba, Christian | |
| dc.contributor.author | Sesma Redín, Rubén | |
| 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 | 2022-01-11T09:29:28Z | |
| dc.date.available | 2022-01-11T09:29:28Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The detection of building footprints and road networks has many useful applications including the monitoring of urban development, real-time navigation, etc. Taking into account that a great deal of human attention is required by these remote sensing tasks, a lot of effort has been made to automate them. However, the vast majority of the approaches rely on very high-resolution satellite imagery (<2.5 m) whose costs are not yet affordable for maintaining up-to-date maps. Working with the limited spatial resolution provided by high-resolution satellite imagery such as Sentinel-1 and Sentinel-2 (10 m) makes it hard to detect buildings and roads, since these labels may coexist within the same pixel. This paper focuses on this problem and presents a novel methodology capable of detecting building and roads with sub-pixel width by increasing the resolution of the output masks. This methodology consists of fusing Sentinel-1 and Sentinel-2 data (at 10 m) together with OpenStreetMap to train deep learning models for building and road detection at 2.5 m. This becomes possible thanks to the usage of OpenStreetMap vector data, which can be rasterized to any desired resolution. Accordingly, a few simple yet effective modifications of the U-Net architecture are proposed to not only semantically segment the input image, but also to learn how to enhance the resolution of the output masks. As a result, generated mappings quadruplicate the input spatial resolution, closing the gap between satellite and aerial imagery for building and road detection. To properly evaluate the generalization capabilities of the proposed methodology, a data-set composed of 44 cities across the Spanish territory have been considered and divided into training and testing cities. Both quantitative and qualitative results show that high-resolution satellite imagery can be used for sub-pixel width building and road detection following the proper methodology. | 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, and by the Spanish MICIN (PID2019-108392GB-I00/AEI/10.13039/501100011033). | en |
| dc.format.extent | 21 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.3390/rs13163135 | |
| dc.identifier.issn | 2072-4292 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41702 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | |
| dc.relation.ispartof | Remote Sensing, 13 (16) | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/ | |
| dc.relation.publisherversion | http://doi.org/10.3390/rs13163135 | |
| dc.rights | © 2021 by the authors. Creative Commons Attribution | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Building detection | en |
| dc.subject | Convolutional neural networks | en |
| dc.subject | Deep learning | en |
| dc.subject | Remote sensing | en |
| dc.subject | Road detection | en |
| dc.subject | Sentinel-1 | en |
| dc.subject | Sentinel-2 | en |
| dc.title | A deep learning approach to an enhanced building footprint and road detection in high-resolution satellite imagery | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 4c0a0a12-02e3-479d-8562-b5d9a39bab40 | |
| relation.isAuthorOfPublication | 806651ab-0bba-4f13-9b29-2109c4b8b72a | |
| relation.isAuthorOfPublication | 44c7a308-9c21-49ef-aa03-b45c2c5a06fd | |
| relation.isAuthorOfPublication.latestForDiscovery | 4c0a0a12-02e3-479d-8562-b5d9a39bab40 |
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