丢失的石油井的航空图像数据集
Anastasiia Kim1, Teeratorn Kadeethum2, Christine Downs2
1Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, USA. akim@lanl.gov.
Scientific data
|September 17, 2024
概括
一个新的空中图像和细分口罩数据集有助于定位孤儿井,这些井构成环境风险. 该资源支持开发人工智能模型,用于识别美国各地的这些未经记录的井.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 孤儿井,未知或破产的运营商,构成一个重大的环境挑战.
- 在美国,成千上万的孤儿井仍然没有文件,这带来了风险.
- 具有成本效益的方法对于识别这些井和减轻环境损害至关重要.
研究的目的:
- 为孤儿井提供全面的空中图像和相关细分面具数据集.
- 促进深度学习模型的开发,用于自动化孤儿井检测.
- 支持寻找和解决未经记录的孤儿井对环境的影响的努力.
主要方法:
- 汇编了120,948张空中图像 (512x512像素) 记录的孤儿井.
- 包括相应的细分口罩,表明井的存在/不存在.
- 使用来自国家农业图像计划 (NAIP) 的图像,分辨率从0.3m到1m.
- 纳入负面示例和相关元数据 (图像ID,空间分辨率,井坐标).
主要成果:
- 一个大规模的,公开可用的数据集,用于孤儿井研究.
- 该数据集使人工智能模型用于井检测的培训和验证成为可能.
- 提供了改善从空中成像识别环境危害的基础.
结论:
- 本文所介绍的数据集是推动自动化孤井检测的宝贵资源.
- 使用此数据集的深度学习方法可以显著改善未经记录的井的识别.
- 这项工作有助于通过加强发现来减轻孤井造成的环境损害.
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