Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks
Up-to-date mapping of built-up areas is of paramount importance for urban planning, environmental monitoring, and disaster management. In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and o...
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001712 |
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| author | Yu Li Patrick Matgen Marco Chini |
| author_facet | Yu Li Patrick Matgen Marco Chini |
| author_sort | Yu Li |
| collection | DOAJ |
| description | Up-to-date mapping of built-up areas is of paramount importance for urban planning, environmental monitoring, and disaster management. In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and optical data. However, the laborious and expensive task of gathering and maintaining a vast array of diverse training data poses a challenge to the widespread adoption of these methods for large-scale built-up area mapping. This paper presents a two-step framework enabling an automated extraction of built-up areas using Sentinel-1 and Sentinel-2 data. Initially, training data for built-up and non-built-up classes are automatically sampled and labeled from Sentinel-1 and Sentinel-2 data for a given area of interest. Subsequently, a cross-fusion neural network is trained using the samples from the first step to produce a built-up map for the entire study area. To enhance the network’s resilience to label noise, a contextual virtual adversarial training (CVAT) regularization is introduced within the mean-teacher architecture. Our proposed framework was tested on 48 different study areas across the world. Both quantitative and qualitative evaluations demonstrate its robustness and effectiveness for large-scale built-up area extraction. The versatility of our framework in generating accurate and up-to-date built-up information, which is essential for monitoring urban environments and assessing economic losses resulting from natural disasters, is highlighted through comparisons with four state-of-the-art global built-up products: Global Human Settlement Built-up map based on 2018 Sentinel-2 composites (GHS-BUILT-S2), World Settlement Footprint 2019 (WSF 2019), ESA World Cover, and Dynamic World. |
| format | Article |
| id | doaj-art-54a1998c2b2f44cebc02fa52dbcc1a99 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-54a1998c2b2f44cebc02fa52dbcc1a992025-08-20T01:49:12ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910452410.1016/j.jag.2025.104524Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networksYu Li0Patrick Matgen1Marco Chini2Corresponding author.; Department of Environmental Research an Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST)Department of Environmental Research an Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST)Department of Environmental Research an Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST)Up-to-date mapping of built-up areas is of paramount importance for urban planning, environmental monitoring, and disaster management. In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and optical data. However, the laborious and expensive task of gathering and maintaining a vast array of diverse training data poses a challenge to the widespread adoption of these methods for large-scale built-up area mapping. This paper presents a two-step framework enabling an automated extraction of built-up areas using Sentinel-1 and Sentinel-2 data. Initially, training data for built-up and non-built-up classes are automatically sampled and labeled from Sentinel-1 and Sentinel-2 data for a given area of interest. Subsequently, a cross-fusion neural network is trained using the samples from the first step to produce a built-up map for the entire study area. To enhance the network’s resilience to label noise, a contextual virtual adversarial training (CVAT) regularization is introduced within the mean-teacher architecture. Our proposed framework was tested on 48 different study areas across the world. Both quantitative and qualitative evaluations demonstrate its robustness and effectiveness for large-scale built-up area extraction. The versatility of our framework in generating accurate and up-to-date built-up information, which is essential for monitoring urban environments and assessing economic losses resulting from natural disasters, is highlighted through comparisons with four state-of-the-art global built-up products: Global Human Settlement Built-up map based on 2018 Sentinel-2 composites (GHS-BUILT-S2), World Settlement Footprint 2019 (WSF 2019), ESA World Cover, and Dynamic World.http://www.sciencedirect.com/science/article/pii/S1569843225001712Built-up areaCross-attentionSARData fusionConvolutional neural networksNoisy label learning |
| spellingShingle | Yu Li Patrick Matgen Marco Chini Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks International Journal of Applied Earth Observations and Geoinformation Built-up area Cross-attention SAR Data fusion Convolutional neural networks Noisy label learning |
| title | Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks |
| title_full | Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks |
| title_fullStr | Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks |
| title_full_unstemmed | Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks |
| title_short | Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks |
| title_sort | extraction of built up areas using sentinel 1 and sentinel 2 data with automated training data sampling and label noise robust cross fusion neural networks |
| topic | Built-up area Cross-attention SAR Data fusion Convolutional neural networks Noisy label learning |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001712 |
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