Research
Themes: Hydrology - Coastal & Marine - Forests & Ecosystems - Cryosphere - Geodesy & Subsurface - AI Methods
BuckAI's research program applies deep learning, computer vision, and physics-informed AI to large scientific datasets — from the full spectrum of satellite modalities to astronomical surveys and high-dimensional numerical simulations. While Earth observation is our primary focus, our six themes extend from applied remote sensing to the development and advancement of AI algorithms for science more broadly. Affiliated faculty and students span Earth Sciences, Geography, Mathematics, Astronomy, Ecology, and Environmental Sciences.
River Classification at Sub-Meter Resolution
Fully convolutional neural networks trained on 30 cm WorldView/QuickBird commercial imagery achieve >90% precision and recall for river detection. Enables tracking of Arctic river width changes and snowmelt-driven discharge at seasonal to annual timescales.
FCN WorldView - Remote Sensing - Env. 2022
PI: Moortgat
Super-Resolution Water Mapping
Deep neural networks applied to free Sentinel-2 multispectral imagery produce 2 m effective water classification maps, extending high-resolution monitoring to the global, cloud-free Sentinel archive rather than costly commercial data
Sentinel-2 - Super-resolution - J. Hydrology 2023
PI: Moortgat
River Discharge from Hypsometry + AI
Combining river channel geometry (hypsometry) with multi-temporal satellite imagery to improve remote estimates of river discharge — bridging the gap between satellite observation and hydrological modeling needs.
SWOT - Remote Sensing Env. 2024
PIs: Moortgat, Durand, Alsdorf
Critical Zone Hydrology & Remote Sensing
AI analysis of multi-sensor satellite data (VIIRS, MODIS, GRACE) for large-scale monitoring of land-water-vegetation interactions, including changes in terrestrial water storage and land surface hydrology under climate change.
GRACE - MODIS - Critical Zone
PIs: Costa, Alsdorf, Yeo
River Classification at Sub-Meter Resolution
Fully convolutional neural networks trained on 30 cm WorldView/QuickBird commercial imagery achieve >90% precision and recall for river detection. Enables tracking of Arctic river width changes and snowmelt-driven discharge at seasonal to annual timescales.
FCN WorldView - Remote Sensing - Env. 2022
PI: Moortgat
Super-Resolution Water Mapping
Deep neural networks applied to free Sentinel-2 multispectral imagery produce 2 m effective water classification maps, extending high-resolution monitoring to the global, cloud-free Sentinel archive rather than costly commercial data
Sentinel-2 - Super-resolution - J. Hydrology 2023
PI: Moortgat
River Discharge from Hypsometry + AI
Combining river channel geometry (hypsometry) with multi-temporal satellite imagery to improve remote estimates of river discharge — bridging the gap between satellite observation and hydrological modeling needs.
SWOT - Remote Sensing Env. 2024
PIs: Moortgat, Durand, Alsdorf
Critical Zone Hydrology & Remote Sensing
AI analysis of multi-sensor satellite data (VIIRS, MODIS, GRACE) for large-scale monitoring of land-water-vegetation interactions, including changes in terrestrial water storage and land surface hydrology under climate change.
GRACE - MODIS - Critical Zone
PIs: Costa, Alsdorf, Yeo
Coastal Bathymetry via Physics-Informed AI
Physics-informed neural networks (PINNs) fusing ICESat-2 lidar, Sentinel-2 multispectral imagery, and hydrodynamic constraints to map shallow-water depths with unprecedented accuracy. NSF CAIG proposal pending ($1.5M, 2026–2029).
PINNs - ICESat-2 - Sentinel-2 - WACV 2025
PI: Moortgat
Sea-Level Change & Satellite Geodesy
Combining GRACE gravity data, satellite altimetry, and GPS measurements to separate ice-mass loss, thermal expansion, and hydrological contributions to sea-level rise — with AI used to improve signal separation and downscaling.
GRACE/GRACE-FO - Altimetry - Sea Level
PIs: Shum, Gómez
Coral Reef & Ocean Heat Stress Monitoring
AI analysis of infrared and microwave satellite data (NOAA CoralTemp, MODIS) to detect ocean heat stress events, map coral bleaching risk, and track long-term reef ecosystem change — supporting conservation and fisheries management.
MODIS - Ocean Heat Stress - Coral Bleaching
PI: Costa
Wetland Mapping & Carbon Dynamics
Multi-sensor satellite analysis to map wetland extent, inundation dynamics, and carbon fluxes — critical for both climate mitigation accounting and coastal ecosystem management.
SAR - Optical - Carbon
PI: Yeo
Coastal Bathymetry via Physics-Informed AI
Physics-informed neural networks (PINNs) fusing ICESat-2 lidar, Sentinel-2 multispectral imagery, and hydrodynamic constraints to map shallow-water depths with unprecedented accuracy. NSF CAIG proposal pending ($1.5M, 2026–2029).
PINNs - ICESat-2 - Sentinel-2 - WACV 2025
PI: Moortgat
Sea-Level Change & Satellite Geodesy
Combining GRACE gravity data, satellite altimetry, and GPS measurements to separate ice-mass loss, thermal expansion, and hydrological contributions to sea-level rise — with AI used to improve signal separation and downscaling.
GRACE/GRACE-FO - Altimetry - Sea Level
PIs: Shum, Gómez
Coral Reef & Ocean Heat Stress Monitoring
AI analysis of infrared and microwave satellite data (NOAA CoralTemp, MODIS) to detect ocean heat stress events, map coral bleaching risk, and track long-term reef ecosystem change — supporting conservation and fisheries management.
MODIS - Ocean Heat Stress - Coral Bleaching
PI: Costa
Wetland Mapping & Carbon Dynamics
Multi-sensor satellite analysis to map wetland extent, inundation dynamics, and carbon fluxes — critical for both climate mitigation accounting and coastal ecosystem management.
SAR - Optical - Carbon
PI: Yeo
Deforestation & Agroforestry in Sub-Saharan Africa
AI-driven satellite monitoring of forest loss and agroforestry expansion in Uganda and the Sahel, supporting CGIAR/SPIA food security and climate mitigation research ($1.25M, 2025–2028). Uses Sentinel-1/2, Landsat, and PlanetScope imagery with semantic segmentation models.
Sentinel - Semantic Segmentation - CGIAR/SPIA - Active
PI: Moortgat
Forest Carbon Stocks & Biomass from LiDAR
Combining spaceborne LiDAR (GEDI, ICESat-2) with optical and SAR data and deep learning to estimate forest biomass, carbon stocks, and forest structure at national and global scales — inputs to carbon crediting and climate models.
GEDI - LiDAR - Forest Biomass
PI: Zhao
Biodiversity & Species Distribution Modeling
Machine learning models linking remote sensing variables (vegetation structure, land cover, phenology) to species occurrence data, enabling spatial predictions of biodiversity patterns and responses to land-use and climate change.
Macroecology - Species Distribution - Trait-Based Ecology
PI: Jarzyna
Land Use, Land Cover & Urban Change
AI methods for large-scale land use and land cover classification, urban expansion mapping, and analysis of human-environment interactions — linking spatial data to socioeconomic and environmental outcomes.
LULC - Urban Remote Sensing - GIS
PI: D. Liu
Deforestation & Agroforestry in Sub-Saharan Africa
AI-driven satellite monitoring of forest loss and agroforestry expansion in Uganda and the Sahel, supporting CGIAR/SPIA food security and climate mitigation research ($1.25M, 2025–2028). Uses Sentinel-1/2, Landsat, and PlanetScope imagery with semantic segmentation models.
Sentinel - Semantic Segmentation - CGIAR/SPIA - Active
PI: Moortgat
Forest Carbon Stocks & Biomass from LiDAR
Combining spaceborne LiDAR (GEDI, ICESat-2) with optical and SAR data and deep learning to estimate forest biomass, carbon stocks, and forest structure at national and global scales — inputs to carbon crediting and climate models.
GEDI - LiDAR - Forest Biomass
PI: Zhao
Biodiversity & Species Distribution Modeling
Machine learning models linking remote sensing variables (vegetation structure, land cover, phenology) to species occurrence data, enabling spatial predictions of biodiversity patterns and responses to land-use and climate change.
Macroecology - Species Distribution - Trait-Based Ecology
PI: Jarzyna
Land Use, Land Cover & Urban Change
AI methods for large-scale land use and land cover classification, urban expansion mapping, and analysis of human-environment interactions — linking spatial data to socioeconomic and environmental outcomes.
LULC - Urban Remote Sensing - GIS
PI: D. Liu
Ice Sheet Dynamics & Glacial Retreat
Multi-sensor analysis of Greenland and Antarctic ice sheet change — combining optical, SAR, and altimetry data with deep learning to map ice velocity, margin retreat, surface mass balance, and subglacial hydrology at continental scale.
Sentinel-1/2 - ICESat-2 - Ice Dynamics
PI: Howat
Mountain Glaciers & Water Security
AI and remote sensing to quantify glacier mass balance, retreat rates, and downstream water availability in the Andes and Himalaya — regions where millions depend on glacial meltwater for agriculture and drinking water.
Andes - Himalaya - Water Security
PI: Mark
Snow Remote Sensing & Runoff Prediction
Machine learning models for snow water equivalent estimation from multi-frequency microwave and optical imagery — improving seasonal streamflow forecasts critical for water resource management in snowmelt-dominated basins.
AMSR - Snow Water Equivalent - Streamflow
PIs: Durand, Quiring
Ice Mass Balance from Satellite Geodesy
Combining GRACE gravity time-series, satellite altimetry, and GPS uplift measurements to separate ice-mass loss from hydrological changes and glacial isostatic adjustment — with AI improving signal decomposition.
GRACE - Glacial Isostasy - GPS
PIs: Shum, Gómez
Ice Sheet Dynamics & Glacial Retreat
Multi-sensor analysis of Greenland and Antarctic ice sheet change — combining optical, SAR, and altimetry data with deep learning to map ice velocity, margin retreat, surface mass balance, and subglacial hydrology at continental scale.
Sentinel-1/2 - ICESat-2 - Ice Dynamics
PI: Howat
Mountain Glaciers & Water Security
AI and remote sensing to quantify glacier mass balance, retreat rates, and downstream water availability in the Andes and Himalaya — regions where millions depend on glacial meltwater for agriculture and drinking water.
Andes - Himalaya - Water Security
PI: Mark
Snow Remote Sensing & Runoff Prediction
Machine learning models for snow water equivalent estimation from multi-frequency microwave and optical imagery — improving seasonal streamflow forecasts critical for water resource management in snowmelt-dominated basins.
AMSR - Snow Water Equivalent - Streamflow
PIs: Durand, Quiring
Ice Mass Balance from Satellite Geodesy
Combining GRACE gravity time-series, satellite altimetry, and GPS uplift measurements to separate ice-mass loss from hydrological changes and glacial isostatic adjustment — with AI improving signal decomposition.
GRACE - Glacial Isostasy - GPS
PIs: Shum, Gómez
Natural Hydrogen Reservoir Prospecting
Semantic segmentation of "fairy circles" (semicircular surface depressions, or SCDs) using Segment Anything Model (SAM) + Sentinel-2 + Copernicus DEM for global mapping of potential natural hydrogen reservoirs. Covered by New Scientist, EurekAlert, and 10+ outlets; industry partner Koloma Inc. ARPA-E subcontract active.
SAM - Sentinel-2 - Natural Hydrogen - Active
PI: Moortgat
GPS Seismology & Earthquake Monitoring
Real-time and near-real-time processing of high-rate GNSS data for seismic waveform extraction, earthquake source characterization, and tsunami early warning — including AI methods for rapid magnitude and slip estimation.
GNSS - Earthquake - Tsunami Warning
PI: Crowell
Satellite Gravity & Geoid Modeling
Advanced processing and AI-assisted interpretation of GRACE/GRACE-FO gravity time-series for hydrology, oceanography, solid Earth, and cryosphere applications — including improved downscaling and signal separation methods.
GRACE-FO - Geoid - Geodesy
PIs: Shum, Gómez
Critical Zone & Subsurface Geochemistry
AI integration of remote sensing with in-situ measurements to study weathering, hydrology, and biogeochemical cycling in Earth's critical zone — from surface vegetation to bedrock — and track changes driven by climate and land use.
Critical Zone - VIIRS - Geochemistry
PI: Costa
Natural Hydrogen Reservoir Prospecting
Semantic segmentation of "fairy circles" (semicircular surface depressions, or SCDs) using Segment Anything Model (SAM) + Sentinel-2 + Copernicus DEM for global mapping of potential natural hydrogen reservoirs. Covered by New Scientist, EurekAlert, and 10+ outlets; industry partner Koloma Inc. ARPA-E subcontract active.
SAM - Sentinel-2 - Natural Hydrogen - Active
PI: Moortgat
GPS Seismology & Earthquake Monitoring
Real-time and near-real-time processing of high-rate GNSS data for seismic waveform extraction, earthquake source characterization, and tsunami early warning — including AI methods for rapid magnitude and slip estimation.
GNSS - Earthquake - Tsunami Warning
PI: Crowell
Satellite Gravity & Geoid Modeling
Advanced processing and AI-assisted interpretation of GRACE/GRACE-FO gravity time-series for hydrology, oceanography, solid Earth, and cryosphere applications — including improved downscaling and signal separation methods.
GRACE-FO - Geoid - Geodesy
PIs: Shum, Gómez
Critical Zone & Subsurface Geochemistry
AI integration of remote sensing with in-situ measurements to study weathering, hydrology, and biogeochemical cycling in Earth's critical zone — from surface vegetation to bedrock — and track changes driven by climate and land use.
Critical Zone - VIIRS - Geochemistry
PI: Costa
Scientific Machine Learning & Uncertainty Quantification
Developing neural network architectures and training strategies tailored to scientific problems: learning from small labeled datasets, quantifying prediction uncertainty, enforcing physical constraints, and constructing data-driven reduced-order models for complex dynamical systems.
Scientific ML - UQ - Dynamical Systems
PI: Xiu
AI for Large Astronomical Surveys
Self-supervised learning, generative models, and foundation model approaches for spectroscopic and photometric survey data. Developing methods that transfer across observational modalities and enable efficient scientific inference from petabyte-scale astronomical archives.
Self-Supervised Learning - Foundation Models - Spectroscopy
PI: Ting
ML-Enhanced Numerical Methods
Combining classical numerical analysis with modern machine learning to build faster, more accurate solvers for differential equations and high-dimensional problems. Applications include hybrid PDE solvers, learned preconditioners, and neural-network-accelerated simulations.
Numerical Analysis - PDEs - Hybrid Solvers
PI: Han Veiga
Cross-Domain Foundation Models
A cross-cutting effort bringing together methods expertise (Mathematics, Astronomy) and domain data (Earth observation, ecology) to build and fine-tune foundation models that generalize across scientific disciplines — a key long-term goal of the Observatory.
Foundation Models - Transfer Learning - Cross-Domain
PIs: Xiu, Ting, Han Veiga, Moortgat
Scientific Machine Learning & Uncertainty Quantification
Developing neural network architectures and training strategies tailored to scientific problems: learning from small labeled datasets, quantifying prediction uncertainty, enforcing physical constraints, and constructing data-driven reduced-order models for complex dynamical systems.
Scientific ML - UQ - Dynamical Systems
PI: Xiu
AI for Large Astronomical Surveys
Self-supervised learning, generative models, and foundation model approaches for spectroscopic and photometric survey data. Developing methods that transfer across observational modalities and enable efficient scientific inference from petabyte-scale astronomical archives.
Self-Supervised Learning - Foundation Models - Spectroscopy
PI: Ting
ML-Enhanced Numerical Methods
Combining classical numerical analysis with modern machine learning to build faster, more accurate solvers for differential equations and high-dimensional problems. Applications include hybrid PDE solvers, learned preconditioners, and neural-network-accelerated simulations.
Numerical Analysis - PDEs - Hybrid Solvers
PI: Han Veiga
Cross-Domain Foundation Models
A cross-cutting effort bringing together methods expertise (Mathematics, Astronomy) and domain data (Earth observation, ecology) to build and fine-tune foundation models that generalize across scientific disciplines — a key long-term goal of the Observatory.
Foundation Models - Transfer Learning - Cross-Domain
PIs: Xiu, Ting, Han Veiga, Moortgat