15 citations · 50 across the 27 of their papers we have counts for
27 papers
Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery
Federica Torrisi, Claudia Corradino, Alessandro Grilli +5
Recent advances in quantum computing are opening new possibilities for Earth Observation (EO) data analysis. Quantum machine learning (QML) approaches offer novel ways to process i…
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…
Quantum Latent Diffusion Models
Francesca De Falco, Andrea Ceschini, Alessandro Sebastianelli +2
The introduction of quantum concepts is increasingly making its way into generative machine learning models. However, while there are various implementations of quantum Generative…
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…
Towards Efficient Quantum Hybrid Diffusion Models
Francesca De Falco, Andrea Ceschini, Alessandro Sebastianelli +2
In this paper, we propose a new methodology to design quantum hybrid diffusion models, derived from classical U-Nets with ResNet and Attention layers. Specifically, we propose two…
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…
Estimation of Ground NO2 Measurements from Sentinel-5P Tropospheric Data through Categorical Boosting
Francesco Mauro, Luigi Russo, Fjoralba Janku +2
This study aims to analyse the Nitrogen Dioxide (NO2) pollution in the Emilia Romagna Region (Northern Italy) during 2019, with the help of satellite retrievals from the Sentinel-5…
Analysis of COVID-19 first wave in the US based on demographic, mobility, and environmental variables
Dario Spiller, Gabriele Santin, Alessandro Sebastianelli +6
COVID-19 had a strong and disruptive impact on our society, and yet further analyses on most relevant factors explaining the spread of the pandemic are needed. Interdisciplinary st…
A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset
Veronica Wairimu Muriga, Benjamin Rich, Francesco Mauro +2
Climate change and increases in drought conditions affect the lives of many and are closely tied to global agricultural output and livestock production. This research presents a no…
SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis
Francesco Mauro, Benjamin Rich, Veronica Wairimu Muriga +2
Climate change has caused disruption in certain weather patterns, leading to extreme weather events like flooding and drought in different parts of the world. In this paper, we pro…
On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Alessandro Sebastianelli, Daniela A. Zaidenberg, Dario Spiller +2
This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote s…
On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery
Maria Pia Del Rosso, Alessandro Sebastianelli, Dario Spiller +2
In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one o…
Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification
Alessandro Sebastianelli, Maria Pia Del Rosso, Pierre Philippe Mathieu +1
Data fusion is a well-known technique, becoming more and more popular in the Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its ability of reinforcing A…
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
Alessandro Sebastianelli, Artur Nowakowski, Erika Puglisi +5
Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study. Related techniques have been analy…
A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks
Alessandro Sebastianelli, Maria Pia Del Rosso, Silvia Liberata Ullo +1
In recent years, machine learning (ML) algorithms have become widespread in all the fields of remote sensing (RS) and earth observation (EO). This has allowed the rapid development…
Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller +2
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applicat…
AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures based on Artificial Intelligence algorithms and multi-sources Data Processing
A. Sebastianelli, F. Mauro, G. Di Cosmo +3
Aim of this paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for th…
A New Mask R-CNN Based Method for Improved Landslide Detection
Silvia Liberata Ullo, Amrita Mohan, Alessandro Sebastianelli +4
This paper presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with tr…