探索河水对岩石的磨损和洞穴效应产生的自然坑洞数据集
Rohini Jadhav1, Sandip Thite2, Shital Pawar3
1Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.
Data in brief
|September 18, 2024
概括
这一数据集提供了3992张自然河流坑的图像,并对机器学习进行了注释. 它通过使坑检测和分析成为可能,有助于地球形态学和水文学的研究.
科学领域:
- 地质形态学 地质形态学
- 水文学的水文学
- 河流科学 河流科学
- 机器学习 机器学习
- 环境科学 环境科学
- 地理空间分析的研究.
背景情况:
- 河流环境中的自然坑是地理形态特征,对于了解河流过程至关重要.
- 现有的数据集可能缺乏该领域高级机器学习应用所需的特异性或注释细节.
研究的目的:
- 介绍和描述河流环境中的自然坑数据集.
- 为地球形态学和相关领域的研究提供高分辨率的注释图像集合.
- 促进机器学习模型的开发,用于坑洞检测和分析.
主要方法:
- 收集了3992张河流环境中的天然坑道的高分辨率图像.
- 使用YOLO (你只看一次) 对象检测框架对图像进行注释.
- 提供XML格式的注释,以便与机器学习算法兼容.
主要成果:
- 现在可以获得一个包含3992张注释的坑洞图像的全面数据集.
- 准确的边界框坐标和每个检测到的坑洞的类标签.
- 该数据集捕捉了各种环境环境中的各种沟特征.
结论:
- 天然坑数据集是促进河流地形学和水文学的研究的宝贵资源.
- 它支持机器学习的应用,用于自动化坑洞检测,分类和预测建模.
- 这一数据集通过对自然坑洞的详细分析,丰富了对地形学过程的理解.
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