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实现可持续的沿海管理:用于高分辨率 Sargassoum 地图的空中图像和深度学习

Javier Arellano-Verdejo¹, Hugo E Lazcano-Hernandez²

¹Department of Observation and Study of the Earth, Atmosphere, and Ocean, El Colegio de la Frontera Sur, Chetumal, Quintana Roo, Mexico.

|September 27, 2024

概括

一种新方法使用空中图像和pix2pix AI来绘制沙上的Sargassum海藻. 这有助于理解和管理影响沿海地区的大规模萨尔加索姆流入.

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:

自2011年以来,大规模的海上沙流入造成了重大的环境和社会挑战.
准确量化和分布绘制海岸上的石灰岩对于管理和处置战略至关重要.

研究的目的:

开发和验证一种方法来计算沙上的石灰岩面积,使用空中图像的语义细分.
评估pix2pix架构的性能,以检测和量化石.

主要方法:

利用一个自定义的数据集,包括来自墨西哥Mahahual和Puerto Morelos的15,268张细分空中图像.
采用了pix2pix深度学习架构用于Sargassum的语义细分.
使用fβ-score和f0.5-score指标评估算法性能,并对置信区间进行启动.

主要成果:

pix2pix算法显示了Sargassum检测的假阳性和负值之间的平衡,有轻微的低估倾向.
该算法在直接对沙区域进行细分时表现最好.
创建了生成的地图,说明了沙沿海的萨加索姆覆盖面.

结论:

拟议的方法有效地量化了使用人工智能驱动的图像细分的萨尔加索姆海.
该研究提供了有价值的工具,以了解石灰岩分布,并为管理策略提供信息.
进一步精制可以提高准确性,特别是在复杂的沿海环境中.

关键词:

藻类的开花藻类的开花人工神经网络的人工神经网络沙监控沙监控沙监控沙监控加勒比海是加勒比海的一个海域.无人机成像无人机成像地理信息系统是一个地理信息系统.遥感是一种远程传感.

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