We are inundated with environmental data – Earth observing satellites stream terabytes of data back to us daily; ground-based sensor networks track weather, water quality and air pollution, taking readings every few minutes; and community scientists log hundreds and thousands of observations every day, recording everything from bird sightings to road closures and accidents. But this very richness of data has created a new set of problems.
This last post in our four-part series gives a brief summary of the data skills that geoscientists will need to develop to effectively work with data in a data-rich, connected, open-source world. This report is loosely based on the town halls and open-source sessions, as well as the more formal Earth and Space Science Informatics sessions, at the AGU fall meeting in Dec 2016.

Data Skills
Twenty-first century science is marked by the availability of huge environmental datasets, unprecedented access to computing power, and an urgent need to understand – and mitigate – the increasing impact of human society on the environment. What skills do geoscientists need to face the challenges and opportunities of 21^st century science? We describe three areas that have the potential to leverage today’s data and computing power to meet our current environmental challenges.

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We are inundated with environmental data – Earth observing satellites stream terabytes of data back to us daily; ground-based sensor networks track weather, water quality and air pollution, taking readings every few minutes; and community scientists log hundreds and thousands of observations every day, recording everything from bird sightings to road closures and accidents. But this very richness of data has created a new set of problems.
This third post in our four-part series gives a brief summary of how deep learning is being used in the geosciences today – loosely based on the Earth and Space Science Informatics sessions and town halls at the AGU fall meeting in Dec 2016.

Deep learning
Artificial Neural Networks (ANNs) are already being widely used in domains ranging from stock price predictions to image recognition; from genetic sequencing to targeted marketing. Deep learning neural networks – that is, networks that have multiple layers of neurons between the input and output neurons – are also beginning to be used in the Geosciences to address a range of problems.

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