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The massive arrival of pelagic Sargassum on the coasts of several countries of the Atlantic Ocean began in 2011 and to date continues to generate social and environmental challenges for the region. Therefore, knowing the distribution and quantity of Sargassum in the ocean, coasts, and beaches is necessary to understand the phenomenon and develop protocols for its management, use, and final disposal. In this context, the present study proposes a methodology to calculate the area Sargassum occupies on beaches in square meters, based on the semantic segmentation of aerial images using the pix2pix architecture. For training and testing the algorithm, a unique dataset was built from scratch, consisting of 15,268 aerial images segmented into three classes. The images correspond to beaches in the cities of Mahahual and Puerto Morelos, located in Quintana Roo, Mexico. To analyze the results the fβ-score metric was used. The results for the Sargassum class indicate that there is a balance between false positives and false negatives, with a slight bias towards false negatives, which means that the algorithm tends to underestimate the Sargassum pixels in the images. To know the confidence intervals within which the algorithm performs better, the results of the f0.5-score metric were resampled by bootstrapping considering all classes and considering only the Sargassum class. From the above, we found that the algorithm offers better performance when segmenting Sargassum images on the sand. From the results, maps showing the Sargassum coverage area along the beach were designed to complement the previous ones and provide insight into the field of study.

The unusual arrival of Sargassum on Caribbean beaches is an emerging problem that has generated numerous challenges. The monitoring, visualization, and estimation of Sargassum coverage on the beaches remain a constant complication. This study proposes a new mapping methodology to estimate Sargassum coverage on the beaches. Semantic segmentation of geotagged photographs allows the generation of accurate maps showing the percent coverage of Sargassum. The first dataset of segmented Sargassum images was built for this study and used to train the proposed model. The results demonstrate that the currently proposed method has an accuracy of 91%, improving on the results reported in the state-of-the-art method where data was also collected through a crowdsourcing scheme, in which only information on the presence and absence of Sargassum is displayed.

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep neural network (named ERISNet) was designed specifically to detect these macroalgae along the coastline through remote sensing support. A new dataset which includes pixel values with and without Sargassum was built to train and test ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90% of probability in its classification skills. ERISNet provides a novel insight to detect accurately algal blooms arrivals.