数据科学竞赛用于从空中遥感数据中跨站点识别单个树种
Sarah J Graves1, Sergio Marconi2, Dylan Stewart3
1Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, United States.
PeerJ
|December 25, 2023
概括
一个数据科学竞赛通过遥感数据进行了先进的树种分类. 一种神经网络方法表现出色,但与新网站和不常见物种作斗争,突出了数据需求.
科学领域:
- 森林生态森林生态学
- 遥感是一种远程传感.
- 机器学习 机器学习
背景情况:
- 来自遥感的单个树冠数据为森林组成和结构提供了洞察力.
- 从大区域的遥感数据中将单个树木归类为物种是一个重大挑战.
- 现有的物种分类方法通常对较少见的物种和应用于新地理区域的物种表现不佳.
研究的目的:
- 用遥感数据确定有效的方法来将单个树冠分类为物种身份.
- 评估不同分类方法在多个地点普遍化和转移到未经训练的区域的能力.
- 建立一个基准来评估树种分类方法的进步.
主要方法:
- 组织了一场数据科学比赛,使用来自三个网站的数据.
- 参与者开发并提交了用于单个树种分类的方法.
- 用准确度,宏观F1得分和交叉损失来评估性能,以2017年的竞争方法作为基线.
主要成果:
- 一个两阶段完全连接的神经网络实现了最佳性能,显著超过了基线组合方法.
- 方法对训练有素的场地很好地进行了概括 (准确率为0.46-0.55),但对未经训练的场地表现出很差的可转移性 (准确率为0.07-0.32).
- 分类准确度受训练数据的可用性影响,常见物种被更好地分类而不是不常见的物种. 同一个属或息地的物种之间经常发生错误.
结论:
- 数据科学竞赛可以推动生态应用的方法进步,例如树种的分类.
- 有效的概括和将学习转移到新的环境仍然是基于遥感的物种识别的关键挑战.
- 改善罕见物种的分类和提高新型遗址的模型稳定性是未来研究的关键领域.
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