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使用深度学习分析建筑特征的建筑立面数据集
Seunghyeon Wang1, Sangkyun Park1, Sungman Park1
1Department of Architectural Engineering, Hanyang University, Seungdong-Gu, Seoul 133791, Korea.
Data in brief
|September 23, 2024
概括
这项研究引入了用于训练深度学习模型的构建立面图像的新数据集. 该资源有助于自动化从静态街景图像 (SSVIs) 分析建筑特征.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 建筑工程 建筑工程 建筑工程
背景情况:
- 建筑特征对于建筑管理和建筑设计至关重要.
- 静态街景图像 (SSVIs) 提供了一种使用深度学习分析建筑特征的非侵入性方法.
- 在公开可用的标记数据集中存在很大的差距,用于训练建筑立面图像的深度学习模型.
研究的目的:
- 要构建一个小说,标记数据集的建筑立面图像.
- 促进深度学习模型的训练,用于自动化建筑特征分析.
- 支持四项具体任务:楼层数的分类,建筑类型的分类,外材料的分类和可用的SSVI分类.
主要方法:
- 来自英国伦敦和苏格兰的外墙图像.
- 专家对特定建筑特征的图像进行注释.
- 为深度学习模型开发量身定制的数据集构建.
主要成果:
- 已经创建了一个标记建筑立面图像的综合数据集.
- 该数据集适合用于训练各种分类任务的深度学习模型.
- 原始数据可以重新用于额外的分析,并进行进一步的注释.
结论:
- 开发的数据集解决了对标记建筑立面数据的需求.
- 这个资源可以从SSVIs中自动分析建筑特征.
- 该数据集对未来的城市信息学和建筑研究研究有很大的潜力.
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