Research record

Publications

Selected work in geospatial AI, urban flooding, and climate research. For the full record, visit Google Scholar.

See the complete record on Google Scholar

2026
Geospatial foundation models
Harvesting AlphaEarth: Benchmarking the geospatial foundation model for agricultural downstream tasks
Yuchi Ma, Yawen Shen, Anu Swatantran, David B. Lobell
International Journal of Applied Earth Observation and Geoinformation, 149, 105258
Evaluating AlphaEarth embeddings for crop yield prediction and mapping agricultural practices, including how well they transfer across locations.
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2026
Learning across scales
STaPL: Scale Transfer with Pseudo-Labelling for satellite-based mapping of agricultural practices
Yuchi Ma, Yawen Shen, Anu Swatantran, Courtland Kelly, David B. Lobell
Remote Sensing of Environment, 343, 115500
Using county-level statistics and satellite observations to map tillage practices without field-level labels for training.
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2024
Computer vision for flooding
Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network
Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall
Environmental Modelling & Software, 173, 105939
Extracting flood extent from camera images with deep learning, and examining the challenges of real storm conditions.
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2022
Climate & infrastructure
Dynamic Modeling of Inland Flooding and Storm Surge on Coastal Cities under Climate Change Scenarios: Transportation Infrastructure Impacts in Norfolk, Virginia USA as a Case Study
Yawen Shen, Navid Tahvildari, Mohamed M. Morsy, Chris Huxley, T. Donna Chen, Jonathan Lee Goodall
Geosciences, 12(6), 224
Modeling how combined inland flooding and storm surge affect a coastal city’s transportation network under climate change scenarios.
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2020
Machine learning & physical models
Training Machine Learning Surrogate Models From a High-Fidelity Physics-Based Model: Application for Real-Time Street-Scale Flood Prediction in an Urban Coastal Community
Faria T. Zahura, Jonathan L. Goodall, Jeffrey M. Sadler, Yawen Shen, Mohamed M. Morsy, Madhur Behl
Water Resources Research, 56(10), e2019WR027038
Training machine learning surrogates on detailed physics-based simulations to support real-time, street-scale flood prediction.
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2019
Compound flood risk
Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall
Yawen Shen, Mohamed M. Morsy, Chris Huxley, Navid Tahvildari, Jonathan L. Goodall
Journal of Hydrology, 579, 124159
Studying the combined effects of storm tide and heavy rainfall to understand flood risk and guide mitigation in coastal watersheds.
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2019
Infrastructure deterioration
Condition State–Based Civil Infrastructure Deterioration Model on a Structure System Level
Yawen Shen, Jonathan L. Goodall, Steven B. Chase
Journal of Infrastructure Systems, 25(1), 04018042
Modeling how interactions between structural components affect infrastructure deterioration, with uncertainty estimates to inform maintenance decisions.
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Publications

Harvesting AlphaEarth: Benchmarking the geospatial foundation model for agricultural downstream tasks

STaPL: Scale Transfer with Pseudo-Labelling for satellite-based mapping of agricultural practices

Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network

Dynamic Modeling of Inland Flooding and Storm Surge on Coastal Cities under Climate Change Scenarios: Transportation Infrastructure Impacts in Norfolk, Virginia USA as a Case Study

Training Machine Learning Surrogate Models From a High-Fidelity Physics-Based Model: Application for Real-Time Street-Scale Flood Prediction in an Urban Coastal Community

Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall

Condition State–Based Civil Infrastructure Deterioration Model on a Structure System Level