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.

Themes

Project Lead

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.

Project Lead

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

Project Lead

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

Project Lead

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.

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.

Featured Blogs

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    ImgSPEC

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Team Members

Matthew Rossi

Research Scientist

Lise Ann St. Denis

Research Scientist

Cutting-Edge Earth Analytics, Earth Data Across Scales

Virginia Iglesias

Research Scientist

Cutting-Edge Earth Analytics, Adaptation Science, Extremes & Natural Hazards

Michael Koontz

Research Scientist

Cutting-Edge Earth Analytics, Earth Data Across Scales

Natasha Stavros

Analytics Hub Director

Cutting-Edge Earth Analytics, Earth Data Across Scales, Extremes & Natural Hazards

Claire Monteleoni

Associate Professor

Cutting-Edge Earth Analytics, Earth Data Across Scales

Ty Tuff

Data Scientist

Analytics, Cutting-Edge Earth Analytics