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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07476 |
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| _version_ | 1866912319507791872 |
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| author | Xu, Yan Zhang, Zhenqiang Zhou, Zhiwei Geng, Liting Li, Yue Li, Jintao |
| author_facet | Xu, Yan Zhang, Zhenqiang Zhou, Zhiwei Geng, Liting Li, Yue Li, Jintao |
| contents | Digital images of Chinas maps play a crucial role in map detection, particularly in ensuring national sovereignty, territorial integrity, and map compliance. However, there is currently no publicly available dataset specifically dedicated to problematic maps the CME dataset. Existing datasets primarily focus on general map data and are insufficient for effectively identifying complex issues such as national boundary misrepresentations, missing elements, and blurred boundaries. Therefore, this study creates a Problematic Map dataset that covers five key problem areas, aiming to provide diverse samples for problematic map detection technologies, support high-precision map compliance detection, and enhance map data quality and timeliness. This dataset not only provides essential resources for map compliance, national security monitoring, and map updates, but also fosters innovation and application of related technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07476 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CMEdataset Advancing China Map Detection and Standardization with Digital Image Resources Xu, Yan Zhang, Zhenqiang Zhou, Zhiwei Geng, Liting Li, Yue Li, Jintao Computer Vision and Pattern Recognition Artificial Intelligence Digital images of Chinas maps play a crucial role in map detection, particularly in ensuring national sovereignty, territorial integrity, and map compliance. However, there is currently no publicly available dataset specifically dedicated to problematic maps the CME dataset. Existing datasets primarily focus on general map data and are insufficient for effectively identifying complex issues such as national boundary misrepresentations, missing elements, and blurred boundaries. Therefore, this study creates a Problematic Map dataset that covers five key problem areas, aiming to provide diverse samples for problematic map detection technologies, support high-precision map compliance detection, and enhance map data quality and timeliness. This dataset not only provides essential resources for map compliance, national security monitoring, and map updates, but also fosters innovation and application of related technologies. |
| title | CMEdataset Advancing China Map Detection and Standardization with Digital Image Resources |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.07476 |