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 scientist 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 Content


Introduction to the Google Earth Engine code editor

Google Earth Engine is a geospatial processing platform which allows for the visualization and analysis of data on a planetary scale.

Featured Content


Computing and plotting 2d spatial point density in R

It is often useful to quickly compute a measure of point density and show it on a map.

Featured Content


Introduction to the Google Earth Engine Python API

In addition to the web-based IDE Google Earth Engine also provides a Python API that can be used on your local machine without the need to utilize


Project Lead

The Global Social Sensing Project is developing new data sources and methods for curating data at scale related to the societal impact and social disruption of natural hazard events. This data is critically important for use in the Earth Sciences, natural hazards research, and in real-time response.

Project Lead

On the heels of the data revolution, science is undergoing a model revolution to harvest scientific understanding from the modern data deluge. Modern models must scale to massive data, leverage spatiotemporal coherence in heterogeneous data, embed scientific knowledge, and answer science questions.

Project Lead

Deep learning has changed our modern experience, from automatically tagging your friends in your pictures, to powering autonomous vehicles that map our world. Increasingly, science applications of deep learning have enabled new insights at massive scale amidst the earth data deluge.

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

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. Projects in Earth Lab using drones span forested environment recovery and structure mapping to landscape dynamics, and data integration with airborne and satellite imagery.

Team Members

Joe McGlinchy

Remote Sensing Scientist

Cutting-Edge Earth Analytics, Earth Data Across Scales

Lise Ann St. Denis

Research Scientist

Cutting-Edge Earth Analytics

Kris Karnauskas

Associate Professor

Cutting-Edge Earth Analytics

Carol Wessman


Cutting-Edge Earth Analytics, Extremes & Natural Hazards

Natasha Stavros

Analytics Hub Director

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

Ally Fitts

Remote Sensing Intern

Cutting-Edge Earth Analytics