Towards sustainable coastal management: aerial imagery and deep learning for high-resolution mapping.

The massive arrival of pelagic 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 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 occupies on beaches in square meters, based on the semantic segmentation of aerial images using the pix2pix architecture.

Use of semantic segmentation for mapping on beaches.

The unusual arrival of on Caribbean beaches is an emerging problem that has generated numerous challenges. The monitoring, visualization, and estimation of coverage on the beaches remain a constant complication. This study proposes a new mapping methodology to estimate coverage on the beaches.

Collective view: mapping distribution along beaches.

The atypical arrival of pelagic 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 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.

Off-line and on-line optical monitoring of microalgal growth.

The growth of microalgae cultures was successfully monitored, using classic off-line optical techniques (optical density and fluorescence) and on-line analysis of digital images. In this study, we found that the chlorophyll fluorescence ratio / has a linear correlation with the logarithmic concentration of microalgae. By using digital images, the biomass concentration correlated with the luminosity of the images through an exponential equation and the length of penetration of a super luminescent blue beam (λ = 440 nm) through an inversely proportional function.

ERISNet: deep neural network for detection along the coastline of the Mexican Caribbean.

Recently, Caribbean coasts have experienced atypical massive arrivals of pelagic with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic 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.