Paolo Gamba - IEEE Xplore Author Profile
Subscribe
ADVANCED SEARCH

Author details

< Back

Paolo Gamba

Also published under: P. Gamba, Paulo Gamba, Paolo E. Gamba

Publications
369
Citations
6,730
Publications by Year
19962025
Co-Authors:
Show All Co-Authors (384)
Image of author Paolo Gamba

Paolo Gamba

Also published under: P. Gamba, Paulo Gamba, Paolo E. Gamba

Affiliation

Department of Electrical
Computer and Biomedical Engineering
University of Pavia, Pavia, Italy

Publication Topics

Biography

Paolo Gamba is Professor at the University of Pavia, Italy in the Telecommunications and Remote Sensing Laboratory. He received the Laurea degree in Electronic Engineering “cum laude” from the University of Pavia, Italy, in 1989, and the Ph.D. in Electronic Engineering from the same University in 1993. He served as Editor-in-Chief of the IEEE Geoscience and Remote Sensing Letters from 2009 to 2013, and as Chair of the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society (GRSS) from October 2005 to May 2009. He has been elected in the GRSS AdCom from 2014 to 2022, served as GRSS President from 2019 to 2020, and as Editor-in-Chief of the IEEE Geoscience and Remote Sensing Magazine in 2023-2024. He also served as Technical C...
Publications
369
Citations
6,730
Publications by Year
19962025
Co-Authors:
Show All Co-Authors (384)
Author's Published Works

Showing 1-25 of 369 results

  • Sort

Filter Results

Show

Sort By

Results

Enhancing Local Climate Zone Classification With MSCA-MSLCZNet: A Multistream Deep Learning Approach

Ximing Liu;Luigi Russo;Wenbo Li;Alim Samat;Silvia Liberata Ullo;Paolo Gamba

IEEE Geoscience and Remote Sensing Letters
Year: 2025 | Volume: 22 | Journal Article |
Accurate local climate zone (LCZ) classification is essential for urban climate studies, environmental monitoring, and sustainable city planning. Recent advances in deep learning (DL) have significantly improved LCZ mapping, but challenges remain in capturing spatial position features and distinguishing spectrally similar land cover types. This letter proposes multiscale coordinate attention-based...Show More

Enhancing Local Climate Zone Classification With MSCA-MSLCZNet: A Multistream Deep Learning Approach

Ximing Liu;Luigi Russo;Wenbo Li;Alim Samat;Silvia Liberata Ullo;Paolo Gamba

Year: 2025 | Volume: 22 | Journal Article |

A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features

Luigi Russo;Antonietta Sorriso;Silvia Liberata Ullo;Paolo Gamba

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2025 | Volume: 18 | Journal Article |
Land cover (LC) mapping using satellite imagery is critical for environmental monitoring and management. Deep learning (DL), particularly convolutional neural networks (CNNs) and vision transformers (ViTs), have revolutionized this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformer-based Swin-Unet architecture with seasonal synthesized s...Show More

A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features

Luigi Russo;Antonietta Sorriso;Silvia Liberata Ullo;Paolo Gamba

Year: 2025 | Volume: 18 | Journal Article |

Quanv4EO: Empowering Earth Observation by Means of Quanvolutional Neural Networks

Alessandro Sebastianelli;Francesco Mauro;Giulia Ciabatti;Dario Spiller;Bertrand Le Saux;Paolo Gamba;Silvia Liberata Ullo

IEEE Transactions on Geoscience and Remote Sensing
Year: 2025 | Volume: 63 | Journal Article |
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 applications use those data, such as natural hazard monitoring, global climate change, urban planning, and more. Many challenges are brought by the use of these big data in the context of remote sensing (RS) applications. I...Show More

Quanv4EO: Empowering Earth Observation by Means of Quanvolutional Neural Networks

Alessandro Sebastianelli;Francesco Mauro;Giulia Ciabatti;Dario Spiller;Bertrand Le Saux;Paolo Gamba;Silvia Liberata Ullo

Year: 2025 | Volume: 63 | Journal Article |

Adaptive Multitask Autoencoder-Based Hyperspectral Unmixing Exploiting Auxiliary Data via Graph Associations

Jia Chen;Jun Li;Paolo Gamba

IEEE Transactions on Geoscience and Remote Sensing
Year: 2025 | Volume: 63 | Journal Article |
Cited by: Papers (2)
Hyperspectral unmixing is a technique in hyperspectral image processing that decomposes the spectra of mixed pixels into pure spectral components (endmembers) and their corresponding contributions (abundances). When dealing with complex mixed-terrain scenes, such as urban areas, significant challenges arise due to the complexity of the environment. Urban areas feature intricate geometric structure...Show More

Adaptive Multitask Autoencoder-Based Hyperspectral Unmixing Exploiting Auxiliary Data via Graph Associations

Jia Chen;Jun Li;Paolo Gamba

Year: 2025 | Volume: 63 | Journal Article |

A Dual-Pol SAR-Based Index for Rice Transplantation Detection

Abhinav Verma;Avik Bhattacharya;Dipankar Mandal;Carlos López-Martínez;Paolo Gamba

IEEE Geoscience and Remote Sensing Letters
Year: 2025 | Volume: 22 | Journal Article |
Detecting rice transplantation dates is crucial for understanding its effect on grain yield and water consumption at regional scales. Traditionally, identifying the rice transplantation phase using dual-polarized (dual-pol) synthetic aperture radar (SAR) data has relied on backscatter intensity due to its characteristic low values during the flooding stage. This study leverages a recently proposed...Show More

A Dual-Pol SAR-Based Index for Rice Transplantation Detection

Abhinav Verma;Avik Bhattacharya;Dipankar Mandal;Carlos López-Martínez;Paolo Gamba

Year: 2025 | Volume: 22 | Journal Article |

Analysis of Integrated Differential Absorption Radar and Subterahertz Satellite Communications Beyond 6G

Sergi Aliaga;Marco Lanzetti;Vitaly Petrov;Anna Vizziello;Paolo Gamba;Josep M. Jornet

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2024 | Volume: 17 | Journal Article |
The integration of sub-Terahertz (sub-THz) communication beyond $\mathbf {100 }\text{ GHz}$ with differential absorption radar (DAR) as part of the evolution toward 5G-sdvanced and 6G nonterrestrial networks (NTNs) and beyond is critical for enabling intrinsic coexistence between these technologies. This study presents the first comprehensive analysis of an integrated sensing and communication (IS...Show More

Analysis of Integrated Differential Absorption Radar and Subterahertz Satellite Communications Beyond 6G

Sergi Aliaga;Marco Lanzetti;Vitaly Petrov;Anna Vizziello;Paolo Gamba;Josep M. Jornet

Year: 2024 | Volume: 17 | Journal Article |

Target Characterization and Scattering Power Components From Dual-Pol Sentinel-1 SAR Data

Abhinav Verma;Avik Bhattacharya;Subhadip Dey;Armando Marino;Paolo Gamba

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Target characterization parameters are pivotal in accurately identifying and assessing diverse land cover targets in radar polarimetry. While full-polarimetric (full-pol) synthetic aperture radar (SAR) data offer numerous parameters, characterizing targets with HH-HV or VV-VH dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity alone due to limited polarimetric i...Show More

Target Characterization and Scattering Power Components From Dual-Pol Sentinel-1 SAR Data

Abhinav Verma;Avik Bhattacharya;Subhadip Dey;Armando Marino;Paolo Gamba

Year: 2024 | Volume: 62 | Journal Article |

Enhancing the Spatial Resolution of Hyperspectral Images Combining High-Accuracy Surface Modeling and Subpixel Unmixing

Jia Chen;Jun Li;Paolo Gamba

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Hyperspectral sensors can rapidly acquire high-quality spectral data, very useful for urban monitoring applications. Unfortunately, their spatial detail is not fine enough, and methods to enhance this resolution are required. However, conventional super-resolution (SR) methods for multispectral data do not match the requirements needed to maintain high spectral fidelity. Therefore, this article pr...Show More

Enhancing the Spatial Resolution of Hyperspectral Images Combining High-Accuracy Surface Modeling and Subpixel Unmixing

Jia Chen;Jun Li;Paolo Gamba

Year: 2024 | Volume: 62 | Journal Article |

Using Multi-Temporal Sentinel-1 and Sentinel-2 Data for Water Bodies Mapping

Luigi Russo;Francesco Mauro;Babak Memar;Alessandro Sebastianelli;Paolo Gamba;Silvia Liberata Ullo

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability, and posing threats to sustainable development, biodiversity, and access to water and sanitation. This paper aims to provide valuable insights for comprehensive water resource monitoring under diverse meteorological conditions. An extension of the SEN2DWATER dataset is proposed t...Show More

Using Multi-Temporal Sentinel-1 and Sentinel-2 Data for Water Bodies Mapping

Luigi Russo;Francesco Mauro;Babak Memar;Alessandro Sebastianelli;Paolo Gamba;Silvia Liberata Ullo

Year: 2024 | Conference Paper |

Mapping Gated Neighborhoods in the Metropolitan Area of Buenos Aires Using Deep Learning and VHR Multispectral Images

Axel Elseser;Juan Cabral;Priscilla Minotti;Paolo Gamba

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Gated Neighborhoods are residential areas characterized primarily by being closed urbanized spaces, with low construction density, and low-rise buildings, as well as significant areas dedicated to green spaces and sometimes to bodies of water. They are widely spread in large urban areas across South America, and they can be found in many locations within the Metropolitan Area of Buenos Aires, Arge...Show More

Mapping Gated Neighborhoods in the Metropolitan Area of Buenos Aires Using Deep Learning and VHR Multispectral Images

Axel Elseser;Juan Cabral;Priscilla Minotti;Paolo Gamba

Year: 2024 | Conference Paper |

Qspecklefilter: A Quantum Machine Learning Approach for SAR Speckle Filtering

Francesco Mauro;Alessandro Sebastianelli;Maria Pia Del Rosso;Paolo Gamba;Silvia Liberata Ullo

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Cited by: Papers (1)
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 regardless of weather conditions, and at any time of day or night. However, SAR imagery quality is often compromised by speckle, a granular disturbance that poses challenges in producing accurate results without suitable ...Show More

Qspecklefilter: A Quantum Machine Learning Approach for SAR Speckle Filtering

Francesco Mauro;Alessandro Sebastianelli;Maria Pia Del Rosso;Paolo Gamba;Silvia Liberata Ullo

Year: 2024 | Conference Paper |

A Hybrid MLP-Quantum Approach in Graph Convolutional Neural Networks for Oceanic Niño Index (ONI) Prediction

Francesco Mauro;Alessandro Sebastianelli;Bertrand Le Saux;Paolo Gamba;Silvia Liberata Ullo

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Cited by: Papers (1)
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 (HQGCNN), combining a Graph Convolutional Neural Network (GCNN) with a Quantum Multilayer Perceptron (MLP). The study highlights the potentialities of GCNNs in handling global-scale dependencies and proposes the HQGCNN fo...Show More

A Hybrid MLP-Quantum Approach in Graph Convolutional Neural Networks for Oceanic Niño Index (ONI) Prediction

Francesco Mauro;Alessandro Sebastianelli;Bertrand Le Saux;Paolo Gamba;Silvia Liberata Ullo

Year: 2024 | Conference Paper |

A Novel Deep Learning Prospective Urban Growth Model Applied to Multispectral Satellite Data over the City of Córdoba, Argentina

María Sol Villella;Paolo Gamba

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Prospective urban growth models serve as tools aimed at supporting sustainable city development, informed decision-making and proactive planning, contributing to enhancing livability and resilience in expanding urban areas.This research develops a prospective urban growth model for the Metropolitan Area of Córdoba (Argentina) by integrating multispectral remote sensing data and open-source GIS dat...Show More

A Novel Deep Learning Prospective Urban Growth Model Applied to Multispectral Satellite Data over the City of Córdoba, Argentina

María Sol Villella;Paolo Gamba

Year: 2024 | Conference Paper |

Two-Click-Based Fast Small Object Annotation in Remote Sensing Images

Lu Lei;Zhenyu Fang;Jinchang Ren;Paolo Gamba;Jiangbin Zheng;Huimin Zhao

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (1)
In the remote sensing field, detecting small objects is a pivotal task, yet achieving high performance in deep learning-based detectors heavily relies on extensive data annotation. The challenge intensifies as small objects in remote sensing imagery are typically densely distributed and numerous, leading to a substantial increase in the cost of creating large-scale annotated datasets. This elevate...Show More

Two-Click-Based Fast Small Object Annotation in Remote Sensing Images

Lu Lei;Zhenyu Fang;Jinchang Ren;Paolo Gamba;Jiangbin Zheng;Huimin Zhao

Year: 2024 | Volume: 62 | Journal Article |

Unsupervised Multitemporal Triclass Change Detection

Rogério G. Negri;Alejandro C. Frery;Wallace Casaca;Paolo Gamba;Avik Bhattacharya

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Change detection is a fundamental task that involves assessing changes in a given region over multiple time periods. It has been widely applied across various fields, including monitoring deforestation, urban expansion, and natural disaster analysis. In this article, we address the critical and complex issue of automatically identifying types of changes in land cover using remotely sensed imagery....Show More

Unsupervised Multitemporal Triclass Change Detection

Rogério G. Negri;Alejandro C. Frery;Wallace Casaca;Paolo Gamba;Avik Bhattacharya

Year: 2024 | Volume: 62 | Journal Article |

A Novel Iterative Self-Organizing Pixel Matrix Entanglement Classifier for Remote Sensing Imagery

Guoqing Zhou;Lihuang Qian;Paolo Gamba

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (3)
The previously presented self-organizing pixel entanglement neural network (SOPENN) model only establishes 2-D basis vectors that are orthogonal to each other in the Hilbert space, which cannot sufficiently reflect the spectral information and the entanglement characteristics of pixels in multispectral images. Therefore, an iterative self-organizing pixel matrix entanglement (ISOPME) image classif...Show More

A Novel Iterative Self-Organizing Pixel Matrix Entanglement Classifier for Remote Sensing Imagery

Guoqing Zhou;Lihuang Qian;Paolo Gamba

Year: 2024 | Volume: 62 | Journal Article |

Radar surface scattering index from dual-pol Sentinel-1 SLC and GRD SAR data

Abhinav Verma;Avik Bhattacharya;Subhadip Dey;Carlos López-Martínez;Paolo Gamba

2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Scattering information extraction is crucial for enhanced target characterization. While numerous parameters have been introduced to describe scattering from targets using full-polarimetric SAR data, the challenge lies in obtaining such scattering-type information from dual-polarimetric SAR data. This study presents an ingenious approach to address this challenge. We propose a novel index for char...Show More

Radar surface scattering index from dual-pol Sentinel-1 SLC and GRD SAR data

Abhinav Verma;Avik Bhattacharya;Subhadip Dey;Carlos López-Martínez;Paolo Gamba

Year: 2024 | Conference Paper |

Deep Spatial—Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection

Wenqian Dong;Xiaoyang Wu;Jiahui Qu;Paolo Gamba;Song Xiao;Anna Vizziello;Yunsong Li

IEEE Transactions on Cybernetics
Year: 2024 | Volume: 54, Issue: 12 | Journal Article |
Cited by: Papers (8)
Hyperspectral target detection aims to locate targets of interest in the scene, and deep learning-based detection methods have achieved the best results. However, black box network architectures are usually designed to directly learn the mapping between the original image and the discriminative features in a single data-driven manner, a choice that lacks sufficient interpretability. On the contrar...Show More

Deep Spatial—Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection

Wenqian Dong;Xiaoyang Wu;Jiahui Qu;Paolo Gamba;Song Xiao;Anna Vizziello;Yunsong Li

Year: 2024 | Volume: 54, Issue: 12 | Journal Article |

A Multitask Framework for Hyperspectral Change Detection and Band Reweighting With Unbalanced Contrastive Learning

Xiande Wu;Paolo Gamba;Jie Feng;Ronghua Shang;Xiangrong Zhang;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (11)
Multitask learning has been widely applied in visual learning to significantly enhance the performance. The combination of hyperspectral change detection (HCD) and band reweighting can achieve discriminative feature enhancement for improving detection performance. However, existing multitask models for these two tasks are unidirectional, with band reweighting unable to learn from task guidance. To...Show More

A Multitask Framework for Hyperspectral Change Detection and Band Reweighting With Unbalanced Contrastive Learning

Xiande Wu;Paolo Gamba;Jie Feng;Ronghua Shang;Xiangrong Zhang;Licheng Jiao

Year: 2024 | Volume: 62 | Journal Article |

SpectralGPT: Spectral Remote Sensing Foundation Model

Danfeng Hong;Bing Zhang;Xuyang Li;Yuxuan Li;Chenyu Li;Jing Yao;Naoto Yokoya;Hao Li;Pedram Ghamisi;Xiuping Jia;Antonio Plaza;Paolo Gamba;Jon Atli Benediktsson;Jocelyn Chanussot

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 8 | Journal Article |
Cited by: Papers (292)
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding,...Show More

SpectralGPT: Spectral Remote Sensing Foundation Model

Danfeng Hong;Bing Zhang;Xuyang Li;Yuxuan Li;Chenyu Li;Jing Yao;Naoto Yokoya;Hao Li;Pedram Ghamisi;Xiuping Jia;Antonio Plaza;Paolo Gamba;Jon Atli Benediktsson;Jocelyn Chanussot

Year: 2024 | Volume: 46, Issue: 8 | Journal Article |

A 3-D Fully Convolutional Network Approach for Land Cover Mapping Using Multitemporal Sentinel-1 SAR Data

David Marzi;Javier I. Santtiz Jara;Paolo Gamba

IEEE Geoscience and Remote Sensing Letters
Year: 2024 | Volume: 21 | Journal Article |
Cited by: Papers (2)
Spaceborne temporal sequences of synthetic aperture radar (SAR) data have a definite advantage over multispectral data sequences in terms of continuity and regularity. Still, deep-learning (DL) applications in remote sensing have primarily focused on multispectral data. This work is focused instead on a novel 3-D DL architecture for SAR data sequences. The proposed approach utilizes a trained-from...Show More

A 3-D Fully Convolutional Network Approach for Land Cover Mapping Using Multitemporal Sentinel-1 SAR Data

David Marzi;Javier I. Santtiz Jara;Paolo Gamba

Year: 2024 | Volume: 21 | Journal Article |

Dynamic Low-Rank and Sparse Priors Constrained Deep Autoencoders for Hyperspectral Anomaly Detection

Sheng Lin;Min Zhang;Xi Cheng;Lei Shi;Paolo Gamba;Hai Wang

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (22)
Linear-based low-rank and sparse models (LRSM) and nonlinear-based deep autoencoder (DAE) models have been proven to be effective for the task of anomaly detection (AD) in hyperspectral images (HSIs). The linear-based LRSM is self-explainable, while it may not characterize the complex scenes well. In contrast, the nonlinear-based DAE is able to extract the discriminative features between the backg...Show More

Dynamic Low-Rank and Sparse Priors Constrained Deep Autoencoders for Hyperspectral Anomaly Detection

Sheng Lin;Min Zhang;Xi Cheng;Lei Shi;Paolo Gamba;Hai Wang

Year: 2024 | Volume: 73 | Journal Article |

Transfer Learning With Nonlinear Spectral Synthesis for Hyperspectral Target Detection

Yanzi Shi;Hua Cui;Yaping Yin;Huansheng Song;Yunsong Li;Paolo Gamba

IEEE Transactions on Geoscience and Remote Sensing
Year: 2023 | Volume: 61 | Journal Article |
Cited by: Papers (7)
Spectral distortion severely limits detection performance in hyperspectral imagery, while feature learning with neural networks could provide sufficient capacity to enhance spectral consistency. This article designs an end-to-end hyperspectral target detection (HTD) network based on transfer learning and nonlinear spectral synthesis (TLNSS). We first utilize bilinear mixture model (BMM) to synthes...Show More

Transfer Learning With Nonlinear Spectral Synthesis for Hyperspectral Target Detection

Yanzi Shi;Hua Cui;Yaping Yin;Huansheng Song;Yunsong Li;Paolo Gamba

Year: 2023 | Volume: 61 | Journal Article |

A 3D Dynamical Hybrid Model: Coupling Statistical And Machine Learning Techniques To SAR Time Series Prediction

Renata Rojas Guerra;Fábio M. Bayer;Paolo Gamba

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Year: 2023 | Conference Paper |
Multitemporal satellite images can be represented as a three-dimensional cube. This remote sensing data type require modeling techniques comprising spatial and temporal dependence altogether. This work aims at developing a hybrid framework combining the three-dimensional autoregressive (3D-AR) statistical model and machine learning algorithms to accommodate spatial and temporal correlations in a s...Show More

A 3D Dynamical Hybrid Model: Coupling Statistical And Machine Learning Techniques To SAR Time Series Prediction

Renata Rojas Guerra;Fábio M. Bayer;Paolo Gamba

Year: 2023 | Conference Paper |

Tillage Assessment in Time Series of Spaceborne Radar Data Over Rice Paddy Fields in Northern Italy

D. Marzi;F. Dell’Acqua;P. Gamba

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Food traceability in organic agriculture requires a comprehensive "crop history" that includes information from the moment seedlings begin to sprout. Radar remote sensing could contribute in this framework by providing satellite-observable variables and time sequences that help build a more complete crop history. One possible application of this concept is monitoring tillage techniques, which have...Show More

Tillage Assessment in Time Series of Spaceborne Radar Data Over Rice Paddy Fields in Northern Italy

D. Marzi;F. Dell’Acqua;P. Gamba

Year: 2023 | Conference Paper |

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.