Pushing the limits of Sentinel-2 for building footprint extraction

dc.contributor.authorAyala Lauroba, Christian
dc.contributor.authorAranda, Carlos
dc.contributor.authorGalar Idoate, Mikel
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.date.accessioned2023-05-29T11:42:43Z
dc.date.available2023-05-29T11:42:43Z
dc.date.issued2022
dc.date.updated2023-05-29T11:36:31Z
dc.description.abstractBuilding 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.sponsorshipChristian 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.mimetypeapplication/pdfen
dc.identifier.citationAyala, 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/IGARSS46834.2022.9883103en
dc.identifier.doi10.1109/IGARSS46834.2022.9883103
dc.identifier.isbn978-1-6654-2792-0
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/45354
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium: IEEE; 2022. p.322-325 978-1-6654-2792-0en
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//0011-1408-2020-000008/
dc.relation.publisherversionhttps://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.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectBuilding Detectionen
dc.subjectConvolutional Neural Networksen
dc.subjectDeep Learningen
dc.subjectRemote Sensingen
dc.subjectSentinel-2en
dc.titlePushing the limits of Sentinel-2 for building footprint extractionen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery4c0a0a12-02e3-479d-8562-b5d9a39bab40

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