Welcome to the Geospatial Analysis for Environmental Change (GAEC) Lab!

We are a group of dynamic geospatial scientists working on quantifying surface water and flooding dynamics and flood damage using AI algorithms and time series of Earth observation data, at sub-continental to global scales.

Research Interests

Surface Water

Understand surface water dynamics

Remote Sensing

General research topics related to remote sensing

Flooding

Map flooding events using AI algorithms and quantify flood damage

Climate

Understand the impacts of climate drivers on water systems

Latest News

Dr. Mirela Tulbure Named as a University Faculty Scholar

Dr. Tulbure was recently named as one of the 20 2024-2025 University Faculty Scholars, selected from over 2,500 faculty members for research achievements.

Last updated on Feb 25, 2025 1 min read
New Paper on Non-Permitted Poultry Operations and Environmental Justice

A new paper led by Dr. Tulbure on non-permitted poultry operations has been published in GeoHealth. Dr. Júlio Caineta, current lab postdoc is a co-author.

Last updated on Jan 17, 2025 2 min read
New Paper on Non-Permitted Poultry Operations and Environmental Justice
Lab members attend AGU24 Conference in Washington, D.C.

GAEC lab members Mirela, Rebecca, Mollie and Varun presented at the American Geophysical Union Conference in Washington, D.C. in December.

Last updated on Jan 14, 2025 1 min read
Lab members attend AGU24 Conference in Washington, D.C.
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Recent Publications

Earth Observation Data to Support Environmental Justice: Linking Non-Permitted Poultry Operations to Social Vulnerability Indices
Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste to nearby farmlands, with severe environmental …
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Earth Observation Data to Support Environmental Justice: Linking Non-Permitted Poultry Operations to Social Vulnerability Indices
Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning
Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and …
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Advancing food security: Rice yield estimation framework using time-series satellite data & machine learning
Taking it further: Leveraging pseudo-labels for field delineation across label-scarce smallholder regions
Satellite-based field delineation has entered a quasi-operational stage due to recent advances in machine learning for computer vision. …
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Taking it further: Leveraging pseudo-labels for field delineation across label-scarce smallholder regions
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