We envision a world where environmental scientists can use cutting-edge data analytics to explore global environmental change using big data through a diverse range of perspectives.

We democratize access and use of cutting-edge data analytics by partnering with environmental scientists in academia and industry (public and private). We advise and facilitate use of  novel analytics approaches including machine learning, deep learning, artificial intelligence, natural language processing, as well as bayesian and frequentist statistics for model-based prediction and inference.

We use scalable scientific computing including supercomputers and cloud services from commercial and government providers. As big data users we inform use cases and work with cyberinfrastructure providers to understand scientific use cases and test their environments as early adopters.

Together these efforts enable use of cutting-edge data science to explore global environmental change as it relates to Earth Lab key science focii.

 

Featured Work:

Blog

Climate Futures Toolbox

Making it easier to gain insight from Big Data
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Themes

Project Lead

Cibele Amaral

Earth Lab

With a growing reliance on machine learning to help find patterns in the ever-growing volumes of environmental data, we utilize cutting-edge analytics such as statistics, deep learning, and artificial intelligence.
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Project Lead

Cibele Amaral

Earth Lab

Open source software can create data tools that streamline data access, pre-processing, and curation into standard formats that enable custom, value-added analytics.
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Project Lead

Cibele Amaral

Earth Lab

We use novel analytics for curating cross-scale datasets that integrate these observations to help us better resolve and understand underlying ecological processes.
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Project Lead

Ty Tuff

Earth Lab

Modern science sometimes requires computers with much more power than is available from your average personal computer. At Earth Lab we develop and deploy our analyses in cloud environments to more easily scale our science.
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Project Lead

Ty Tuff

Earth Lab

Drones equipped with high resolution multispectral and RGB cameras can observe the natural environment at a much finer spatial and temporal scale than possible with most satellite imagery.
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Project Lead

Cibele Amaral

Earth Lab

At Earth Lab, we often use data from diverse sources to facilitate inquiry, from the more conventional remote sensing datasets such as multispectral satellite imagery and radar backscatter, airborne lidar data, and high-resolution UAV imagery, to the less traditional datasets such as social media feeds, housing layers, and event databases.
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Project Lead

Cibele Amaral

Earth Lab

From centimeter-scale imagery collected from UAVs, to airborne hyperspectral imagery at the meter-scale, to the 10’s of meter scale from satellite multispectral imaging systems, the diversity of data representing the Earth’s surface at different scales enables us to ask questions from the hyperlocal to continental and global scale. We combine these data to better understand processes and change occurring on the Earth.
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