1 citations · 2 across the 15 of their papers we have counts for
16 papers
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…
Monitoring Post-Disaster Urban Recovery Using High-Resolution SAR Time Series and Unsupervised Learning: Evidence from the 2023 Türkiye-Syria Earthquake
Luigi Russo, Deodato Tapete, Silvia Liberata Ullo +1
Monitoring post-disaster recovery is essential for understanding how urban systems rebuild and progressively return to functionality. However, tracking reconstruction remains diffi…
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…
Enriching Earth Observation labeled data with Quantum Conditioned Diffusion Models
Francesco Mauro, Francesca De Falco, Lorenzo Papa +5
The rapid adoption of diffusion models (DMs) in the Earth Observation (EO) domain has unlocked new generative capabilities aimed at producing new samples, whose statistical propert…
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…
SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking
Luigi Russo, Francesco Mauro, Alessandro Sebastianelli +2
Climate change and increasing droughts pose significant challenges to water resource management around the world. These problems lead to severe water shortages that threaten ecosys…
From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
Andrea Ceschini, Francesco Mauro, Francesca De Falco +7
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challeng…
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks
Alessandro Sebastianelli, Francesco Mauro, Giulia Ciabatti +4
A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different app…
QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering
Francesco Mauro, Alessandro Sebastianelli, Maria Pia Del Rosso +2
The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover r…
A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction
Francesco Mauro, Alessandro Sebastianelli, Bertrand Le Saux +2
This paper explores an innovative fusion of Quantum Computing (QC) and Artificial Intelligence (AI) through the development of a Hybrid Quantum Graph Convolutional Neural Network (…
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…