Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings
Semantic segmentation of building facades has enabled much intelligent support for architectural research and practice in the last decade. Faced with the free facade of modern buildings, however, the accuracy of segmentation decreased significantly, partly due to its low regularity of composition. T...
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MDPI AG
2024-08-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/14/9/2602 |
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| author | Junjie Wei Yuexia Hu Si Zhang Shuyu Liu |
| author_facet | Junjie Wei Yuexia Hu Si Zhang Shuyu Liu |
| author_sort | Junjie Wei |
| collection | DOAJ |
| description | Semantic segmentation of building facades has enabled much intelligent support for architectural research and practice in the last decade. Faced with the free facade of modern buildings, however, the accuracy of segmentation decreased significantly, partly due to its low regularity of composition. The freely organized facade composition is likely to weaken the features of different elements, thus increasing the difficulty of segmentation. At present, the existing facade datasets for semantic segmentation tasks were mostly developed based on the classical facades, which were organized regularly. To train the pixel-level classifiers for the free facade segmentation, this study developed a finely annotated dataset named Irregular Facades (IRFs). The IRFs consist of 1057 high-quality facade images, mainly in the modernist style. In each image, the pixels were labeled into six classes, i.e., Background, Plant, Wall, Window, Door, and Fence. The multi-network cross-dataset control experiment demonstrated that the IRFs-trained classifiers segment the free facade of modern buildings more accurately than those trained with existing datasets. The formers show a significant advantage in terms of average WMIoU (0.722) and accuracy (0.837) over the latters (average WMIoU: 0.262–0.505; average accuracy: 0.364–0.662). In the future, the IRFs are also expected to be considered the baseline for the coming datasets of freely organized building facades. |
| format | Article |
| id | doaj-art-6001fb4bb8d44f93a355c83236da2272 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-6001fb4bb8d44f93a355c83236da22722025-08-20T01:56:11ZengMDPI AGBuildings2075-53092024-08-01149260210.3390/buildings14092602Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern BuildingsJunjie Wei0Yuexia Hu1Si Zhang2Shuyu Liu3College of Architecture, Nanjing Tech University, Nanjing 211816, ChinaCollege of Architecture, Nanjing Tech University, Nanjing 211816, ChinaCollege of Art & Design, Nanjing Tech University, Nanjing 211816, ChinaCollege of Architecture, Nanjing Tech University, Nanjing 211816, ChinaSemantic segmentation of building facades has enabled much intelligent support for architectural research and practice in the last decade. Faced with the free facade of modern buildings, however, the accuracy of segmentation decreased significantly, partly due to its low regularity of composition. The freely organized facade composition is likely to weaken the features of different elements, thus increasing the difficulty of segmentation. At present, the existing facade datasets for semantic segmentation tasks were mostly developed based on the classical facades, which were organized regularly. To train the pixel-level classifiers for the free facade segmentation, this study developed a finely annotated dataset named Irregular Facades (IRFs). The IRFs consist of 1057 high-quality facade images, mainly in the modernist style. In each image, the pixels were labeled into six classes, i.e., Background, Plant, Wall, Window, Door, and Fence. The multi-network cross-dataset control experiment demonstrated that the IRFs-trained classifiers segment the free facade of modern buildings more accurately than those trained with existing datasets. The formers show a significant advantage in terms of average WMIoU (0.722) and accuracy (0.837) over the latters (average WMIoU: 0.262–0.505; average accuracy: 0.364–0.662). In the future, the IRFs are also expected to be considered the baseline for the coming datasets of freely organized building facades.https://www.mdpi.com/2075-5309/14/9/2602free facadeirregular facademodern buildingsemantic segmentationclassifier trainingIRFs |
| spellingShingle | Junjie Wei Yuexia Hu Si Zhang Shuyu Liu Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings Buildings free facade irregular facade modern building semantic segmentation classifier training IRFs |
| title | Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings |
| title_full | Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings |
| title_fullStr | Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings |
| title_full_unstemmed | Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings |
| title_short | Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings |
| title_sort | irregular facades a dataset for semantic segmentation of the free facade of modern buildings |
| topic | free facade irregular facade modern building semantic segmentation classifier training IRFs |
| url | https://www.mdpi.com/2075-5309/14/9/2602 |
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