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ESIP Lab Past Projects

The ESIP Lab funds a cohort of projects each year. Here are previously funded projects.

overview of ocean

Photo by NASA

Small grants make big impact.

Microfunding opens the door to bigger projects. And it’s necessary in today’s research cycle. New ideas and technology need a little money and a lot of space with collaborative support to do the incremental work of pushing the Earth sciences forward.


Total dollars granted through the ESIP Lab each year for pilot projects and FUNding Friday


Total projects funded and guided through the ESIP Lab small-grant innovation process.


Additional funding secured by ESIP Lab pilot projects through NSF, NASA, and NOAA.


Cloud provider credits used by ESIP Lab projects each year.

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Machine Learning for Flood Risk Assessment

Tom Narock | Goucher University

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Integrated Gradient Boosting Decision Trees and Deep Learning For Large-Scale SWE Estimation

Ryan Johnson | University of Alabama

What ESIP Lab funding accomplishes

“We can now incorporate deep learning in hurricane modeling to save lives and reduce damages.”

Assistant Professor , Department of Earth and Atmospheric Science, University of Houston

“With ESIP Lab funding, we transitioned a hydrologic modal from “research grade” to cloud-based operations for watersheds on three continents.”

Assistant Professor , Department of Earth Sciences, Montana State University

“We are developing a sensor network to calibrate hydrology altimetry data for airborne and satellite applications.”

PhD Candidate , Geography Department, UCLA

“We can now produce high-quality crop maps by creating deep learning workflows in web browsers.”

Research Professor , Computer Science Department, George Mason University


  • ESIPhub: Developing and promoting an ESIP community resource for sharing and running scientific workflows via JupyterHub | Sean Gordon
  • Equipping OPeNDAP with data citation functionality | Niklas Griessbaum
  • Enhancing sUAS Data with Semantic Technologies | Andrea Thomer
  • Improve Earth Data Discovery through Deep Query Understanding | Yongyao Jiang



  • An Automatic Approach to Building Earth Science Knowledge Graph to Improve Data Discovery | Lewis McGibbney, NASA Jet Propulsion Laboratory | github | ESIP Blog Post
  • Prototyping a Cloud-based High-Performance Spatial Web Portal for Parallel Analytics of Big Climate Data | Zhenlong Li, University of South Carolina | github
  • Cloud-based computing for predicting streamflow in snowmelt-dominated watersheds | Anne Nolin, Oregon State University | ESIP Blog Post