Coast Sargassum Level Estimation from Smartphone Pictures

TL;DR

This paper presents a method to estimate sargassum levels on Caribbean coasts using smartphone images and deep learning, providing a cost-effective alternative to satellite imagery for ecological monitoring.

Contribution

It introduces a novel ground-level image dataset and demonstrates the effectiveness of fine-tuned VGG networks for sargassum level classification.

Findings

VGG network achieved the best classification accuracy.

The model's predictions are suitable for quick ecological assessments.

Ground-level images can reliably estimate sargassum levels.

Abstract

Since 2011, significant and atypical arrival of two species of surface dwelling algae, Sargassum natans and Sargassum Fluitans, have been detected in the Mexican Caribbean. This massive accumulation of algae has had a great environmental and economic impact. Therefore, for the government, ecologists, and local businesses, it is important to keep track of the amount of sargassum that arrives on the Caribbean coast. High-resolution satellite imagery is expensive or may be time delayed. Therefore, we propose to estimate the amount of sargassum based on ground-level smartphone photographs. From the computer vision perspective, the problem is quite difficult since no information about the 3D world is provided, in consequence, we have to model it as a classification problem, where a set of five labels define the amount. For this purpose, we have built a dataset with more than one thousand examples from public forums such as Facebook or Instagram and we have tested several state-of-the-art convolutional networks. As a result, the VGG network trained under fine-tuning showed the best performance. Even though the reached accuracy could be improved with more examples, the current prediction distribution is narrow, so the predictions are adequate for keeping a record and taking quick ecological actions.