24 citations · 90 across the 43 of their papers we have counts for
5 papers · 2 filters
An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data
Francesca Razzano, Wenyu Yang, Sergio Vitale +3
Accurate forest height estimation is crucial for climate change monitoring and carbon cycle assessment. Synthetic Aperture Radar (SAR), particularly in multi-channel configurations…
A Quantum-assisted Attention U-Net for Building Segmentation over Tunis using Sentinel-1 Data
Luigi Russo, Francesco Mauro, Babak Memar +3
Building segmentation in urban areas is essential in fields such as urban planning, disaster response, and population mapping. Yet accurately segmenting buildings in dense urban re…
An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images
Babak Memar, Luigi Russo, Silvia Liberata Ullo +1
Accurate estimation of building heights using very high resolution (VHR) synthetic aperture radar (SAR) imagery is crucial for various urban applications. This paper introduces a D…
A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake
Luigi Russo, Deodato Tapete, Silvia Liberata Ullo +1
Building damage identification shortly after a disaster is crucial for guiding emergency response and recovery efforts. Although optical satellite imagery is commonly used for disa…
A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features
Luigi Russo, Antonietta Sorriso, Silvia Liberata Ullo +1
Land Cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and V…