The atypical arrival of pelagic Sargassum to the Mexican Caribbean beaches has caused considerable economic and ecological damage. Furthermore, it has raised new challenges for monitoring the coastlines. Historically, satellite remote-sensing has been used for Sargassum monitoring in the ocean; nonetheless, limitations in the temporal and spatial resolution of available satellite platforms do not allow for near real-time monitoring of this macro-algae on beaches. This study proposes an innovative approach for monitoring Sargassum on beaches using Crowdsourcing for imagery collection, deep learning for automatic classification, and geographic information systems for visualizing the results. We have coined this collaborative process “Collective View”. It offers a geotagged dataset of images illustrating the presence or absence of Sargassum on beaches located along the northern and eastern regions in the Yucatan Peninsula, in Mexico. This new dataset is the largest of its kind in surrounding areas. As part of the design process for Collective View, three convolutional neural networks (LeNet-5, AlexNet and VGG16) were modified and retrained to classify images, according to the presence or absence of Sargassum. Findings from this study revealed that AlexNet demonstrated the best performance, achieving a maximum recall of 94%. These results are good considering that the training was carried out using a relatively small set of unbalanced images. Finally, this study provides a first approach to mapping the Sargassum distribution along the beaches using the classified geotagged images and offers novel insight into how we can accurately map the arrival of algal blooms along the coastline.

中文翻译：

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集体观点：绘制沿海滩的Sargassum分布图

远洋的Sargassum非典型地到达墨西哥加勒比海的海滩已经造成了相当大的经济和生态破坏。此外，它在监测海岸线方面也提出了新的挑战。从历史上看，卫星遥感技术已用于海洋中的Sargassum监测。但是，可用卫星平台在时间和空间分辨率上的限制不允许对海滩上的这种大型藻类进行近实时监测。这项研究提出了一种创新的方法，该方法可使用众包进行图像收集，深度学习进行自动分类以及地理信息系统以可视化结果的方式来监视海滩上的Sargassum。我们将这种协作过程称为“集体观点”。它提供了经过地理标记的图像数据集，该图像说明了墨西哥尤卡坦半岛北部和东部地区海滩上是否存在Sargassum。这个新的数据集是周边地区同类中最大的。作为“集体视图”设计过程的一部分，根据是否存在Sargassum，对三个卷积神经网络（LeNet-5，AlexNet和VGG16）进行了修改和重新训练以对图像进行分类。这项研究的结果表明，AlexNet表现出最好的性能，最大召回率达到94％。考虑到训练是使用相对较小的一组不平衡图像进行的，因此这些结果很好。最后，