A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.

The economic valuation of coastal ecosystem services is a critical step for the design of sound public policies that support the preservation of the services that nature provides to society in the context of climate change. Using the value transfer method, we obtained the economic valuation that represents the loss of coastal ecosystem services caused by sea level rise in Mexico. Using the Bathtub method, digital elevation models and sea level data, we identified the areas in the country prone to flooding and the associated ecosystem impacts. In Mexico, the annual economic loss caused by the disappearance of coastal ecosystem services is estimated at $6,476,402,405 USD, where wetlands represent the greatest economic losses, since they represent the largest affected ecosystem by area. However, beaches and dunes are the most valued ecosystem due to the economic activities that occur in these areas. In the mangroves, the service as habitat, refuge and nursery is the most valued for its positive relationship with fisheries. The states with the most economic losses are Baja California Sur, Sinaloa and Campeche. The protection of the coastal zone in Mexico should be a priority in the development strategies in the country because its loss and/or rehabilitation imply high economic costs and compromises the wellbeing of society.