24 citations · 90 across the 43 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…
Detection of Christmas tree plantations from high-resolution aerial imagery. A case study in the French Morvan
Francesca Razzano, Emanuele Dalsasso, Adrien Baysse-Lainé +3
Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS). Their delineation is challenging because of high planting…
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…
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…
DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification
Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano +2
Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information. I…
Transformer-Driven Active Transfer Learning for Cross-Hyperspectral Image Classification
Muhammad Ahmad, Francesco Mauro, Manuel Mazzara +3
Hyperspectral image (HSI) classification presents inherent challenges due to high spectral dimensionality, significant domain shifts, and limited availability of labeled data. To a…
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…
Assessment of Spectral based Solutions for the Detection of Floating Marine Debris
Muhammad Alì, Francesca Razzano, Sergio Vitale +4
Typically, the detection of marine debris relies on in-situ campaigns that are characterized by huge human effort and limited spatial coverage. Following the need of a rapid soluti…
Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification
Muhammad Ahmad, Manuel Mazzara, Salvatore Distifano
3D Swin Transformer (3D-ST) known for its hierarchical attention and window-based processing, excels in capturing intricate spatial relationships within images. Spatial-spectral Tr…
AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission
Francesca Razzano, Pietro Di Stasio, Francesco Mauro +4
Differently from conventional procedures, the proposed solution advocates for a groundbreaking paradigm in water quality monitoring through the integration of satellite Remote Sens…
Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data
Francesca Razzano, Francesco Mauro, Pietro Di Stasio +4
Monitoring water contaminants is of paramount importance, ensuring public health and environmental well-being. Turbidity, a key parameter, poses a significant problem, affecting wa…
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…
Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms
Mariapia Rita Iandolo, Francesca Razzano, Chiara Zarro +2
The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific…
Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine
Francesca Razzano, Mariapia Rita Iandolo, Chiara Zarro +2
In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification. Specifically, the integration of Sentinel-1 (S-1) and Sentinel-…
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…
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 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…