从公民科学和深度学习中对珊瑚礁进行广泛的侦察
Christopher L Lawson1, Kathryn M Chartrand2, Chris M Roelfsema3
1Marine Spatial Ecology Laboratory, Centre for Conservation and Biodiversity Science, School of the Environment, The University of Queensland, Brisbane, Australia. Christopher.lawson@uq.edu.au.
Environmental monitoring and assessment
|June 26, 2025
概括
公民科学和深度学习使用盆地图像准确估计珊瑚覆盖. 这种方法为保护提供可靠的数据,即使资源有限.
科学领域:
- 海洋生物学 海洋生物学
- 保护技术 保护技术
- 公民科学是公民科学.
背景情况:
- 珊瑚礁监测对于保护至关重要,但通常受到规模和资源的限制.
- 越来越需要大规模,最新的数据来优先考虑保护工作.
- 公民科学与新技术相结合,为数据收集提供了一个潜在的解决方案.
研究的目的:
- 评估公民科学计划的准确性和可行性,使用深度学习和对本地图像数据的人类分析.
- 确定这种结合方法用于估计珊瑚覆盖面的有效性.
- 评估准确的场地级评估所需的图像数量.
主要方法:
- 一个公民科学计划收集了大型视野底图像.
- 图像使用深度学习算法和在线公民科学家的组合进行分析.
- 准确性与各类珊瑚的专家评估进行了比较.
主要成果:
- 结合深度学习和公民科学分析,与专家评估相比,对关键珊瑚群体 (分支Acropora,板Acropora,巨型珊瑚) 实现了~99%的准确性.
- 通过18-80图像,可以达到95%的现场准确度.
- "所有其他珊瑚"类别的准确性较低,在特定条件下仅在60%的地点达到95%,在特定条件下仅在60%的地点达到95%.
结论:
- 新兴技术和公民科学为广泛收集珊瑚礁数据提供了一个易于使用和具有成本效益的工具.
- 这种方法可以补充传统的高分辨率调查,或者在资源有限的情况下作为主要工具.
- 为了估计不那么明显的珊瑚类别,需要进一步细化.
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