| Resumo: | As human-induced pressures continue to rise in the coastal zone, there is an increasing need to resourcefully predict, detect and monitor environmental patterns to support large scale conservation strategies. The Portuguese coastal zone is the home to profuse biological communities, including mussels, which are a key ecological species for the biodiversity of seashore ecosystems, supporting and shielding a vast amount of invertebrate species. Additionally, the improvement of unmanned aerial devices and high-resolution aerial photography have provided the possibility to produce large temporal and spatial datasets while subsiding both biological and physical disturbances in the ecosystems. On this basis, a low-altitude and high resolution aerial image set was captured by a research team from the Biology Department of the University of Aveiro to measure the coverage, size and density of mussels along the Portuguese shoreline. With this newly-gathered dataset, a group from the Department of Electronics, Telecommunications and Informatics, from the same institution, took the initiative to create computer vision algorithms through deep learning in order to assist the analysis of the collected data and verify the viability of the data-gathering methods. This work presents all the thorough procedures executed to answer the proposed challenge, from the development of a functional pixel-wise image segmentation dataset, to the development of predicting models using renowned architectures in the deep learning community, capable of achieving good results to enable the understanding of the dynamics of the ecosystem and predict the mussel abundance under distinct environmental scenarios. Furthermore, the solution has the potential to grow and be improved further. By exploring a new dataset that may open new doors for understanding and classification of coastal zones, with models that could potentially be re-trained in the future for different kinds of shores and intertidal zones with more and other animal communities, this work also proves the possibility of using deep learning models to analyze image data acquired from drones and hopes to allow further research on the subject and on different types of areas and vegetation.
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