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Deep learning is a computer analysis technique inspired by the human brain’s ability to learn. It involves several layers of artificial neural networks to learn and subsequently recognize patterns in data, forming the basis of many state-of-the-art applications from self-driving cars to drug discovery and cancer detection. Deep neural networks are capable of learning many levels of abstraction, and thus outperform many other types of automated classification algorithms. This project developed software tools, resources, and two training workshops that will allow USGS scientists to apply deep learning to remotely sensed imagery and to better understand natural hazards and habitats across the Nation. The tools and...
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Dam decommissioning is rapidly emerging as an important river restoration strategy in the U.S., with several major removals recently completed or in progress. But few studies have evaluated the far-reaching consequences of these significant environmental perturbations, especially those resulting from removals of large (>10-15 m tall) structures during the last decade. In particular, interactions between physical and ecological aspects of dam removal are poorly known. From recent work, however, observations are now available from several diverse settings nationwide to allow synthesis of key physical and ecological processes associated with dam removals, including fish and benthic community response, reservoir erosion,...
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