8 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…
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…
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…
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…
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…
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-…