覆盖高度模型和NAIP图像在CONUS中配对
Brady W Allred1, Sarah E McCord2, Scott L Morford3
1Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, USA. brady.allred@umontana.edu.
Scientific data
|February 22, 2025
概括
本研究通过创建美国各地CHM芯片的大数据集来解决不一致的树冠高度模型 (CHM). 这些数据将为大规模的CHM生产和应用提供更好的模型.
科学领域:
- 地理空间科学是一个科学领域.
- 遥感是一种远程传感.
- 生态生态学 生态生态学
背景情况:
- 天花板高度模型 (CHM) 为生态和地理空间应用提供了关键的环境垂直结构数据.
- 现有的CHM往往缺乏时空一致性,限制了它们的使用,需要扩展.
- 由于空间多样性数据不足,缩放CHM具有挑战性.
研究的目的:
- 创建一个全面的CHM芯片数据集,以促进大型CHM生产模型的开发.
- 克服阻碍CHM的时空缩放的数据限制.
主要方法:
- 利用美国地质调查局的3D高度计划激光雷达数据生产了22,796,764个一米分辨率的CHM芯片.
- 在与美国相邻的美国,在占主导地位的土地覆盖类型中分层化CHM芯片.
- 将每个CHM芯片与来自美国农业部国家农业图像计划的时间对齐的空中图像配对.
主要成果:
- 创建了一个庞大的数据集,包括22,796,764个高分辨率的CHM芯片.
- 创建配对的CHM和空中图像数据,涵盖美国各地的各种土地覆盖面.
- 建立了培训模型的基础,以生产大规模的CHM.
结论:
- 创建的数据集解决了对空间多样性的数据的需求,以改善CHM的时空一致性.
- 这种资源对于推进依赖于准确和可扩展的CHM的研究和应用至关重要.
- 能够开发先进的机器学习模型,用于自动化,大规模的CHM生成.
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