A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China
Using street-view imagery for interpreting diverse street-scale elements and their relationships within historical districts offers high efficiency and low cost for preservation and management. Scene graphs provide a structured representation of objects and their relationships within a scene. Howeve...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-11-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-3-2024/209/2024/isprs-archives-XLVIII-3-2024-209-2024.pdf |
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| _version_ | 1850199135798427648 |
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| author | X. Guo X. Liu J. Jiang |
| author_facet | X. Guo X. Liu J. Jiang |
| author_sort | X. Guo |
| collection | DOAJ |
| description | Using street-view imagery for interpreting diverse street-scale elements and their relationships within historical districts offers high efficiency and low cost for preservation and management. Scene graphs provide a structured representation of objects and their relationships within a scene. However, applying existing scene graph generation techniques directly to street-view imagery presents challenges due to the complexity of elements and narrow street spaces. This paper introduces HSSGG (Historical Street-view Scene Graph Generation), a predictive model that effectively identifies elements and their relationships. By incorporating an end-to-end Relation Transformer with the parameter-free attention and coordinate attention modules, HSSGG improves relationship prediction accuracy, even with limited samples, and enhances the precision of scene graph generation in complex environments. Test on 200 panoramic images from historical districts in Beijing shows that HSSGG outperforms existing single-stage relation prediction models (such as RelTR and FCSGG) in accuracy and stability. These results provide valuable insights for the preservation and management of historical districts. |
| format | Article |
| id | doaj-art-6421d69e52ca48e4853d90b9872161ab |
| institution | OA Journals |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-6421d69e52ca48e4853d90b9872161ab2025-08-20T02:12:41ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342024-11-01XLVIII-3-202420921610.5194/isprs-archives-XLVIII-3-2024-209-2024A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, ChinaX. Guo0X. Liu1J. Jiang2School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, ChinaUsing street-view imagery for interpreting diverse street-scale elements and their relationships within historical districts offers high efficiency and low cost for preservation and management. Scene graphs provide a structured representation of objects and their relationships within a scene. However, applying existing scene graph generation techniques directly to street-view imagery presents challenges due to the complexity of elements and narrow street spaces. This paper introduces HSSGG (Historical Street-view Scene Graph Generation), a predictive model that effectively identifies elements and their relationships. By incorporating an end-to-end Relation Transformer with the parameter-free attention and coordinate attention modules, HSSGG improves relationship prediction accuracy, even with limited samples, and enhances the precision of scene graph generation in complex environments. Test on 200 panoramic images from historical districts in Beijing shows that HSSGG outperforms existing single-stage relation prediction models (such as RelTR and FCSGG) in accuracy and stability. These results provide valuable insights for the preservation and management of historical districts.https://isprs-archives.copernicus.org/articles/XLVIII-3-2024/209/2024/isprs-archives-XLVIII-3-2024-209-2024.pdf |
| spellingShingle | X. Guo X. Liu J. Jiang A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China |
| title_full | A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China |
| title_fullStr | A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China |
| title_full_unstemmed | A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China |
| title_short | A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China |
| title_sort | scene graph generation method for historical district street view imagery a case study in beijing china |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-3-2024/209/2024/isprs-archives-XLVIII-3-2024-209-2024.pdf |
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