Pushing the limits of Sentinel-2 for building footprint extraction
| 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.date.accessioned | 2023-05-29T11:42:43Z | |
| dc.date.available | 2023-05-29T11:42:43Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-05-29T11:36:31Z | |
| dc.description.abstract | Building footprint maps are of high importance nowadays since a wide range of services relies on them to work. However, activities to keep these maps up-to-date are costly and time-consuming due to the great deal of human intervention required. Several automation attempts have been carried out in the last decade aiming at fully automatizing them. However, taking into account the complexity of the task and the current limitations of semantic segmentation deep learning models, the vast majority of approaches rely on aerial imagery (<1 m). As a result, prohibitive costs and high revisit times prevent the remote sensing community from maintaining up-to-date building maps. This work proposes a novel deep learning architecture to accurately extract building footprints from high resolution satellite imagery (10 m). Accordingly, super-resolution and semantic segmentation techniques have been fused to make it possible not only to improve the building's boundary definition but also to detect buildings with sub-pixel width. As a result, fine-grained building maps at 2.5 m are generated using Sentinel-2 imagery, closing the gap between satellite and aerial semantic segmentation. | 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.mimetype | application/pdf | en |
| dc.identifier.citation | Ayala, C., Aranda, C., & Galar, M. (2022). Pushing the limits of sentinel-2 for building footprint extraction. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 322-325. https://doi.org/10.1109/IGARSS46834.2022.9883103 | en |
| dc.identifier.doi | 10.1109/IGARSS46834.2022.9883103 | |
| dc.identifier.isbn | 978-1-6654-2792-0 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/45354 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium: IEEE; 2022. p.322-325 978-1-6654-2792-0 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//0011-1408-2020-000008/ | |
| dc.relation.publisherversion | https://doi.org/10.1109/IGARSS46834.2022.9883103 | |
| dc.rights | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Building Detection | en |
| dc.subject | Convolutional Neural Networks | en |
| dc.subject | Deep Learning | en |
| dc.subject | Remote Sensing | en |
| dc.subject | Sentinel-2 | en |
| dc.title | Pushing the limits of Sentinel-2 for building footprint extraction | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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