Multi-class strategies for joint building footprint and road detection in remote sensing
Building footprints and road networks are important inputs for a great deal of services. For instance, building maps are useful for urban planning, whereas road maps are essential for disaster response services. Traditionally, building and road maps are manually generated by remote sensing experts o...
| Autores: | , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/41735 |
| Acesso em linha: | https://hdl.handle.net/2454/41735 |
| Access Level: | acceso abierto |
| Palavra-chave: | Sentinel-1 Sentinel-2 Remote sensing Building detection Road detection Deep learning Convolutional neural networks Multi-class semantic segmentation Binary semantic segmentation Multi-task semantic segmentation |
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Multi-class strategies for joint building footprint and road detection in remote sensing |
| title |
Multi-class strategies for joint building footprint and road detection in remote sensing |
| spellingShingle |
Multi-class strategies for joint building footprint and road detection in remote sensing Ayala Lauroba, Christian Sentinel-1 Sentinel-2 Remote sensing Building detection Road detection Deep learning Convolutional neural networks Multi-class semantic segmentation Binary semantic segmentation Multi-task semantic segmentation |
| title_short |
Multi-class strategies for joint building footprint and road detection in remote sensing |
| title_full |
Multi-class strategies for joint building footprint and road detection in remote sensing |
| title_fullStr |
Multi-class strategies for joint building footprint and road detection in remote sensing |
| title_full_unstemmed |
Multi-class strategies for joint building footprint and road detection in remote sensing |
| title_sort |
Multi-class strategies for joint building footprint and road detection in remote sensing |
| dc.creator.none.fl_str_mv |
Ayala Lauroba, Christian Aranda, Carlos Galar Idoate, Mikel |
| author |
Ayala Lauroba, Christian |
| author_facet |
Ayala Lauroba, Christian Aranda, Carlos Galar Idoate, Mikel |
| author_role |
author |
| author2 |
Aranda, Carlos Galar Idoate, Mikel |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
Institute of Smart Cities - ISC Gobierno de Navarra / Nafarroako Gobernua, 0011-1408-2020-000008 |
| dc.subject.none.fl_str_mv |
Sentinel-1 Sentinel-2 Remote sensing Building detection Road detection Deep learning Convolutional neural networks Multi-class semantic segmentation Binary semantic segmentation Multi-task semantic segmentation |
| topic |
Sentinel-1 Sentinel-2 Remote sensing Building detection Road detection Deep learning Convolutional neural networks Multi-class semantic segmentation Binary semantic segmentation Multi-task semantic segmentation |
| description |
Building footprints and road networks are important inputs for a great deal of services. For instance, building maps are useful for urban planning, whereas road maps are essential for disaster response services. Traditionally, building and road maps are manually generated by remote sensing experts or land surveying, occasionally assisted by semi-automatic tools. In the last decade, deep learning-based approaches have demonstrated their capabilities to extract these elements automatically and accurately from remote sensing imagery. The building footprint and road network detection problem can be considered a multi-class semantic segmentation task, that is, a single model performs a pixel-wise classification on multiple classes, optimizing the overall performance. However, depending on the spatial resolution of the imagery used, both classes may coexist within the same pixel, drastically reducing their separability. In this regard, binary decomposition techniques, which have been widely studied in the machine learning literature, are proved useful for addressing multiclass problems. Accordingly, the multi-class problem can be split into multiple binary semantic segmentation sub-problems, specializing different models for each class. Nevertheless, in these cases, an aggregation step is required to obtain the final output labels. Additionally, other novel approaches, such as multi-task learning, may come in handy to further increase the performance of the binary semantic segmentation models. Since there is no certainty as to which strategy should be carried out to accurately tackle a multi-class remote sensing semantic segmentation problem, this paper performs an in-depth study to shed light on the issue. For this purpose, open-access Sentinel-1 and Sentinel-2 imagery (at 10 m) are considered for extracting buildings and roads, making use of the well-known U-Net convolutional neural network. It is worth stressing that building and road classes may coexist within the same pixel when working at such a low spatial resolution, setting a challenging problem scheme. Accordingly, a robust experimental study is developed to assess the benefits of the decomposition strategies and their combination with a multi-task learning scheme. The obtained results demonstrate that decomposing the considered multi-class remote sensing semantic segmentation problem into multiple binary ones using a One-vs-All binary decomposition technique leads to better results than the standard direct multi-class approach. Additionally, the benefits of using a multi-task learning scheme for pushing the performance of binary segmentation models are also shown. |
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2021 |
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2021 |
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article |
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https://hdl.handle.net/2454/41735 |
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00 |
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https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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MDPI |
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Multi-class strategies for joint building footprint and road detection in remote sensingAyala Lauroba, ChristianAranda, CarlosGalar Idoate, MikelSentinel-1Sentinel-2Remote sensingBuilding detectionRoad detectionDeep learningConvolutional neural networksMulti-class semantic segmentationBinary semantic segmentationMulti-task semantic segmentationBuilding footprints and road networks are important inputs for a great deal of services. For instance, building maps are useful for urban planning, whereas road maps are essential for disaster response services. Traditionally, building and road maps are manually generated by remote sensing experts or land surveying, occasionally assisted by semi-automatic tools. In the last decade, deep learning-based approaches have demonstrated their capabilities to extract these elements automatically and accurately from remote sensing imagery. The building footprint and road network detection problem can be considered a multi-class semantic segmentation task, that is, a single model performs a pixel-wise classification on multiple classes, optimizing the overall performance. However, depending on the spatial resolution of the imagery used, both classes may coexist within the same pixel, drastically reducing their separability. In this regard, binary decomposition techniques, which have been widely studied in the machine learning literature, are proved useful for addressing multiclass problems. Accordingly, the multi-class problem can be split into multiple binary semantic segmentation sub-problems, specializing different models for each class. Nevertheless, in these cases, an aggregation step is required to obtain the final output labels. Additionally, other novel approaches, such as multi-task learning, may come in handy to further increase the performance of the binary semantic segmentation models. Since there is no certainty as to which strategy should be carried out to accurately tackle a multi-class remote sensing semantic segmentation problem, this paper performs an in-depth study to shed light on the issue. For this purpose, open-access Sentinel-1 and Sentinel-2 imagery (at 10 m) are considered for extracting buildings and roads, making use of the well-known U-Net convolutional neural network. It is worth stressing that building and road classes may coexist within the same pixel when working at such a low spatial resolution, setting a challenging problem scheme. Accordingly, a robust experimental study is developed to assess the benefits of the decomposition strategies and their combination with a multi-task learning scheme. The obtained results demonstrate that decomposing the considered multi-class remote sensing semantic segmentation problem into multiple binary ones using a One-vs-All binary decomposition technique leads to better results than the standard direct multi-class approach. Additionally, the benefits of using a multi-task learning scheme for pushing the performance of binary segmentation models are also shown.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).MDPIInstitute of Smart Cities - ISCGobierno de Navarra / Nafarroako Gobernua, 0011-1408-2020-0000082021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/41735reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/417352026-06-17T12:41:47Z |
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