GeoCrack:一种高分辨率的数据集,用于对地质突出的断层边缘进行细分
Mohammed Yaqoob1, Mohammed Ishaq2, Mohammed Yusuf Ansari2,3
1Texas A&M University, Electrical and Computer Engineering, Doha, Qatar. yansari@tamu.edu.
Scientific data
|December 3, 2024
概括
GeoCrack是一个新的开源数据集,它推进了地质断裂细分的深度学习. 它提供了12158个来自不同突起的注释图像对,改善了自然骨折的表征.
科学领域:
- 地质地质地质地质地质地
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 在地质突出的自然断层的特征对于地下分析至关重要.
- 现有的数据集缺乏高级深度学习应用程序的规模和注释细节.
- 断裂细分的挑战包括石质学的变化,气候变化和封闭.
研究的目的:
- 介绍GeoCrack,这是第一个大型的开源注释数据集,用于断裂痕迹细分.
- 启用和标准化基于深度学习的骨折特征.
- 在注释地质断裂数据时解决现实世界的复杂性.
主要方法:
- 收集了来自11个不同的地质遗址的摄影图像.
- 手动细分的骨折痕迹与严格的注释审查过程.
- 创建了12158个图像-面具对 (224x224像素),考虑到地质和环境因素.
主要成果:
- GeoCrack数据集包括12,158个注释图像对的断裂痕迹.
- 数据集捕捉了各种各样的地质条件和注释挑战.
- 一个U-Net模型实现了85%的十字路口超过联盟 (IoU) 断裂细分.
结论:
- GeoCrack为自然断裂特征数据集设定了一个新的标准.
- 该数据集为地质断层识别提供了强大的深度学习模型.
- 它有效地解决了骨折细分的各种现实世界的挑战.
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