一个大型众包街景数据集用于绘制非洲道路表面类型的地图
Qi Zhou1, Jiayuan Duan2, Jie Qiao2
1School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, P. R. China. sevenlaand@hotmail.com.
Scientific data
|June 13, 2025
概括
这项研究从众包图像创建了一个大型,专家标记的道路表面数据集. 深度学习模型实现了高精度,显示了改善道路识别和基础设施开发的潜力.
科学领域:
- 计算机科学 计算机科学
- 地理空间科学 地理空间科学
- 人工智能的人工智能
背景情况:
- 道路表面识别对于基础设施维护和社会经济进步至关重要.
- 众包街景数据提供全球覆盖范围,但缺乏深度学习模型的足够注释数据集.
- 准确的路面分类对于有效的城市规划和发展至关重要.
研究的目的:
- 开发和验证使用众包街景图像进行道路表面分类的大规模,专家注释的数据集.
- 在新创建的道路表面数据集上评估各种深度学习模型的性能.
- 评估这些模型在非洲等地区的道路基础设施评估中的适用性.
主要方法:
- 专家使用投票机制将20万张Mapillary街景图像标记为"铺路"",未铺路"和"未知"类别.
- 在专家标记的数据集上培训和评估五种深度学习模型,包括Swin变压器.
- 使用准确性,精度,回忆力和F1分数进行绩效评估,然后对非洲次区域进行应用.
主要成果:
- 所有训练有素的深度学习模型都表现出高性能,在准确性,精度,回忆和F1.0方面得分超过0.90.
- 斯温变压器模型在专家标记的数据集上达到0.924的最高准确度.
- 当应用于非洲次区域时,表现最好的模型在大多数指标中超过了0.95,这表明了强烈的概括性.
结论:
- 专家标记的众包数据集对于训练道路表面分类的深度学习模型非常有效.
- 深度学习模型,特别是Swin变压器,显示了准确和可扩展的道路表面识别的巨大潜力.
- 这种方法可以对道路基础设施的评估和改进有很大帮助,特别是在像非洲这样的数据稀缺地区.
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