A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite data
Transformer-based semantic segmentation architectures excel in extracting road networks from very-high-resolution (VHR) satellite images due to their ability to capture global contextual information. Nonetheless, there is a gap in research regarding their comparative effectiveness, efficiency, and p...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017224000749 |
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| author | Jan Bolcek Mohamed Barakat A. Gibril Rami Al-Ruzouq Abdallah Shanableh Ratiranjan Jena Nezar Hammouri Mourtadha Sarhan Sachit Omid Ghorbanzadeh |
| author_facet | Jan Bolcek Mohamed Barakat A. Gibril Rami Al-Ruzouq Abdallah Shanableh Ratiranjan Jena Nezar Hammouri Mourtadha Sarhan Sachit Omid Ghorbanzadeh |
| author_sort | Jan Bolcek |
| collection | DOAJ |
| description | Transformer-based semantic segmentation architectures excel in extracting road networks from very-high-resolution (VHR) satellite images due to their ability to capture global contextual information. Nonetheless, there is a gap in research regarding their comparative effectiveness, efficiency, and performance in extracting road networks from multicity VHR data. This study evaluates 11 transformer-based models on three publicly available datasets (DeepGlobe Road Extraction Dataset, SpaceNet-3 Road Network Detection Dataset, and Massachusetts Road Dataset) to assess their performance, efficiency, and complexity in mapping road networks from multicity, multidate, and multisensory VHR optical satellite images. The evaluated models include Unified Perceptual Parsing for Scene Understanding (UperNet) based on the Swin transformer (UperNet-SwinT), and Multi-path Vision Transformer (UperNet-MpViT), Twins transformer, Segmenter, SegFormer, K-Net based on SwinT, Mask2Former based on SwinT (Mask2Former-SwinT), TopFormer, UniFormer, and PoolFormer. Results showed that the models recorded mean F-scores (mF-score) ranging from 82.22% to 90.70% for the DeepGlobe dataset, 58.98%–86.95% for the Massachusetts dataset, and 69.02%–86.14% for the SpaceNet-3 dataset. Mask2Former-SwinT, UperNet-MpViT, and SegFormer were the top performers among the evaluated models. The Mask2Former, based on the SwinT, demonstrated a strong balance of high performance across different satellite image datasets and moderate computational efficiency. This investigation aids in selecting the most suitable model for extracting road networks from remote sensing data. |
| format | Article |
| id | doaj-art-f3455d0ddd214250bba1fae55dfeccdf |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-f3455d0ddd214250bba1fae55dfeccdf2025-01-03T04:08:58ZengElsevierScience of Remote Sensing2666-01722025-06-0111100190A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite dataJan Bolcek0Mohamed Barakat A. Gibril1Rami Al-Ruzouq2Abdallah Shanableh3Ratiranjan Jena4Nezar Hammouri5Mourtadha Sarhan Sachit6Omid Ghorbanzadeh7GIS and Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno-Kralovo pole, 61600 Czech RepublicGIS and Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab EmiratesGIS and Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab EmiratesGIS and Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; Scientific Research Center, Australian University, KuwaitGIS and Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab EmiratesGIS and Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, United Arab EmiratesDepartment of Civil Engineering, College of Engineering, Universiti of Thi-Qar, 64001, Nasiriyah, Thi-Qar, IraqInstitute of Geomatics, University of Natural Resources and Life Sciences, Peter-Jordan Strasse 82,1190 Vienna, Austria; Corresponding author.Transformer-based semantic segmentation architectures excel in extracting road networks from very-high-resolution (VHR) satellite images due to their ability to capture global contextual information. Nonetheless, there is a gap in research regarding their comparative effectiveness, efficiency, and performance in extracting road networks from multicity VHR data. This study evaluates 11 transformer-based models on three publicly available datasets (DeepGlobe Road Extraction Dataset, SpaceNet-3 Road Network Detection Dataset, and Massachusetts Road Dataset) to assess their performance, efficiency, and complexity in mapping road networks from multicity, multidate, and multisensory VHR optical satellite images. The evaluated models include Unified Perceptual Parsing for Scene Understanding (UperNet) based on the Swin transformer (UperNet-SwinT), and Multi-path Vision Transformer (UperNet-MpViT), Twins transformer, Segmenter, SegFormer, K-Net based on SwinT, Mask2Former based on SwinT (Mask2Former-SwinT), TopFormer, UniFormer, and PoolFormer. Results showed that the models recorded mean F-scores (mF-score) ranging from 82.22% to 90.70% for the DeepGlobe dataset, 58.98%–86.95% for the Massachusetts dataset, and 69.02%–86.14% for the SpaceNet-3 dataset. Mask2Former-SwinT, UperNet-MpViT, and SegFormer were the top performers among the evaluated models. The Mask2Former, based on the SwinT, demonstrated a strong balance of high performance across different satellite image datasets and moderate computational efficiency. This investigation aids in selecting the most suitable model for extracting road networks from remote sensing data.http://www.sciencedirect.com/science/article/pii/S2666017224000749Remote sensingRoad extractionSatellite dataSemantic segmentationVision Transformers |
| spellingShingle | Jan Bolcek Mohamed Barakat A. Gibril Rami Al-Ruzouq Abdallah Shanableh Ratiranjan Jena Nezar Hammouri Mourtadha Sarhan Sachit Omid Ghorbanzadeh A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite data Science of Remote Sensing Remote sensing Road extraction Satellite data Semantic segmentation Vision Transformers |
| title | A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite data |
| title_full | A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite data |
| title_fullStr | A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite data |
| title_full_unstemmed | A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite data |
| title_short | A comprehensive evaluation of deep vision transformers for road extraction from very-high-resolution satellite data |
| title_sort | comprehensive evaluation of deep vision transformers for road extraction from very high resolution satellite data |
| topic | Remote sensing Road extraction Satellite data Semantic segmentation Vision Transformers |
| url | http://www.sciencedirect.com/science/article/pii/S2666017224000749 |
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