7 papers
A Unified Framework for Efficient Remote Sensing Visual Question Answering: Adapting Dual, Hybrid, and Encoder-Decoder Architectures
Timothy Agboada, Shikha Chandel, Yadav Raj Ghimire +1
Visual Question Answering (VQA) in the Remote Sensing (RS) domain presents unique challenges due to the high resolution, multi scale object distribution, and semantic complexity of…
Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations
Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada +1
Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery. While most deep learning methods rely on spatial-domain featur…
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…