公民科学家可靠地从无人机图像中计算了危的加拉帕戈斯海
Andrea Varela-Jaramillo1,2, Christian Winkelmann3, Andrés Mármol-Guijarro2
1Institute of Biology, Molecular Evolution and Systematics of Animals, University of Leipzig, Leipzig, Saxony, Germany.
Scientific reports
|July 24, 2025
概括
公民科学 (CS) 志愿者从无人机图像中准确地检测和计数海,在数据精制后达到91-92%的准确性. 虽然有效,但CS分析仍然是资源密集型的,突出显示了对自动化解决方案的需求.
科学领域:
- 保护生物学 保护生物学
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
背景情况:
- 人口调查对保护至关重要,但资源密集.
- 无人机技术有助于数据收集,但增加了分析工作量.
- 公民科学 (CS) 通过吸引非专家提供了数据分析的潜在解决方案.
研究的目的:
- 评估公民科学志愿者在从空中无人机图像中检测和计数海洋的准确性.
- 为了比较CS输入的不同数据聚合方法.
- 确定影响志愿者图像分析准确度的因素.
主要方法:
- 超过13,000名志愿者在动物世界平台上分类了57,838张无人机图像.
- 用于评估的是来自4,345张图像的专家计数的黄金标准数据集.
- 测试了各种聚合方法,包括多数投票和HDBSCAN集群.
主要成果:
- 志愿者检测准确度在68%至94%之间,倾向于低估 (假阴性比假阳性更多).
- 每张图像至少有五名志愿者的门比标准多数投票提高了准确性.
- 除了低于最佳的试点数据,志愿者的准确性提高到91-92%;HDBSCAN集群表现最好.
结论:
- 公民科学志愿者可以准确地识别和计数无人机图像中的海.
- 数据质量和聚合方法显著影响CS准确性.
- 尽管CS有效,但对自动化分析方法的需求仍然对资源效率至关重要.
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