AI-driven remote sensing enhances Mediterranean seagrass monitoring and conservation to combat climate change and anthropogenic impacts.

Seagrasses are undergoing widespread loss due to anthropogenic pressure and climate change. Since 1960, the Mediterranean seascape lost 13-50% of the areal extent of its dominant and endemic seagrass-Posidonia oceanica, which regulates its ecosystem. Many conservation and restoration projects failed due to poor site selection and lack of long-term monitoring. Here, we present a fast and efficient operational approach based on a deep-learning artificial intelligence model using Sentinel-2 data to map the spatial extent of the meadows, enabling short and long-term monitoring, and identifying the impacts of natural and human-induced stressors and changes at different timescales. We apply ACOLITE atmospheric correction to the satellite data and use the output to train the model along with the ancillary data and therefore, map the extent of the meadows. We apply noise-removing filters to enhance the map quality. We obtain 74-92% of overall accuracy, 72-91% of user's accuracy, and 81-92% of producer's accuracy, where high accuracies are observed at 0-25 m depth. Our model is easily adaptable to other regions and can produce maps in in-situ data-scarce regions, providing a first-hand overview. Our approach can be a support to the Mediterranean Posidonia Network, which brings together different stakeholders such as authorities, scientists, international environmental organizations, professionals including yachting agents and marinas from the Mediterranean countries to protect all P. oceanica meadows in the Mediterranean Sea by 2030 and increase each country's capability to protect these meadows by providing accurate and up-to-date maps to prevent its future degradation.

The obscuration by dust of most of the growth of supermassive black holes.

Supermassive black holes underwent periods of exponential growth during which we see them as quasars in the distant Universe. The summed emission from these quasars generates the cosmic X-ray background, the spectrum of which has been used to argue that most black-hole growth is obscured. There are clear examples of obscured black-hole growth in the form of 'type-2' quasars, but their numbers are fewer than expected from modelling of the X-ray background. Here we report the direct detection of a population of distant type-2 quasars, which is at least comparable in size to the well-known unobscured type-1 population. We selected objects that have mid-infrared and radio emissions characteristic of quasars, but which are faint at near-infrared and optical wavelengths. We conclude that this population is responsible for most of the black-hole growth in the young Universe and that, throughout cosmic history, black-hole growth occurs in the dusty, gas-rich centres of active galaxies.