7 papers
Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning
Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery. Traditional methods, which often rely on manual inspe…
Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging
Yadav Raj Ghimire, Jagrati Talreja, Tewodros Syum Gebre +3
In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based mod…
Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery
Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consisten…
Physics-Informed Machine Learning for Short-Term Flood Prediction
Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni
Accurate flood forecasting is essential for mitigating disaster risks and protecting communities. However, purely data-driven machine learning models often struggle in data-scarce…
Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference
Tewodros Syum Gebre, Jagrati Talreja, Matilda Anokye +1
Rapid and accurate flood extent mapping from Remote Sensing data, such as Synthetic Aperture Radar (SAR), is critical for operational disaster response, but standard Deep Learning…
Cross-Polarization Fusion of VV AND VH SAR Observations for Improved Flood Mapping
Jagrati Talreja, Tewodros Syum Gebre, Leila Hashemi Beni
Synthetic Aperture Radar (SAR) imagery is widely used for flood monitoring due to its all-weather and day-night imaging capability. However, flood mapping using single-polarization…