1 citations · 2 across the 15 of their papers we have counts for
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AquaCubeAI-Powered Monitoring Turbidity on-board Φsat-2
Pietro Di Stasio, Francesca Razzano, Elisa Liparulo +6
Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground processing, introducing latenc…
Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in Córdoba, Argentina
Luigi Russo, Anabella Ferral, Silvia Liberata Ullo +1
Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their het…
Context-Aware Slum Mapping in Sub-Saharan Africa Using Sentinel-1 Texture and Local Climate Zones
Peterson Chepkilot, Babak Memar, Paolo Gamba
Accurate mapping of informal settlements remains a major challenge in Sub-Saharan African (SSA) cities because optical imagery often fails to distinguish Informal Settlements (defi…
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…
Benchmarking of a new data splitting method on volcanic eruption data
Simona Reale, Pietro Di Stasio, Francesco Mauro +3
In this paper, a novel method for data splitting is presented: an iterative procedure divides the input dataset of volcanic eruption, chosen as the proposed use case, into two part…
Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping
Luigi Russo, Francesco Mauro, Babak Memar +3
Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability, and posing threats to sustainable development, biodiversity…