遥感地面储数据集用于对象检测和基础设施评估
Celine Robinson1, Kyle Bradbury2, Mark E Borsuk3
1Department of Civil and Environmental Engineering and Duke Center on Risk, Duke University, Durham, North Carolina, 27708, USA. celine.robinson@duke.edu.
Scientific data
|January 12, 2024
概括
这项研究引入了来自远程传感图像的13万多个地面储 (AST) 的新数据集. 本资源帮助机器学习进行工业基础设施分析和风险评估.
科学领域:
- 地理空间分析是什么?
- 遥感是一种远程传感.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 遥感图像的数量和可访问性越来越大.
- 缺乏注释数据阻碍了用于机器学习的自动化分析.
- 需要用于工业基础设施评估的标准化数据集.
研究的目的:
- 开发一个新的,公开可用的,多类数据集的地面储 (AST).
- 通过机器学习来促进工业基础设施的大规模自动化分析.
主要方法:
- 高分辨率,远程传感图像的注释.
- 一个数据集的开发,包括地理空间坐标,边界顶点,直径和正确的图像.
- 将超过13万个AST分为五个不同的类别进行分类.
主要成果:
- 创建一个全面的数据集,包括13万多个AST在连续的美国.
- 数据集包括五个标记类别:外部浮动屋顶水箱,封闭屋顶水箱,球形压力水箱,沉积水箱和水塔.
- 为每个AST提供了详细的注释,使得各种分析应用成为可能.
结论:
- 新的AST数据集解决了对遥感中注释数据的关键需求.
- 允许直接使用或训练机器学习模型进行风险评估,能力估计和基础设施评估.
- 通过公开可用的数据促进工业设施自动化分析的进步.
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