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STEFANO VINCENZI

COLLABORATORE DI RICERCA
Dipartimento di Ingegneria "Enzo Ferrari"

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Pubblicazioni

2023 - Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks [Articolo su rivista]
Bonicelli, Lorenzo; Porrello, Angelo; Vincenzi, Stefano; Ippoliti, Carla; Iapaolo, Federica; Conte, Annamaria; Calderara, Simone
abstract

2021 - The color out of space: learning self-supervised representations for Earth Observation imagery [Relazione in Atti di Convegno]
Vincenzi, Stefano; Porrello, Angelo; Buzzega, Pietro; Cipriano, Marco; Fronte, Pietro; Cuccu, Roberto; Ippoliti, Carla; Conte, Annamaria; Calderara, Simone
abstract

The recent growth in the number of satellite images fosters the development of effective deep-learning techniques for Remote Sensing (RS). However, their full potential is untapped due to the lack of large annotated datasets. Such a problem is usually countered by fine-tuning a feature extractor that is previously trained on the ImageNet dataset. Unfortunately, the domain of natural images differs from the RS one, which hinders the final performance. In this work, we propose to learn meaningful representations from satellite imagery, leveraging its high-dimensionality spectral bands to reconstruct the visible colors. We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor. Furthermore, we qualitatively observe that guesses based on natural images and colorization rely on different parts of the input. This paves the way to an ensemble model that eventually outperforms both the above-mentioned techniques.

2021 - Training convolutional neural networks to score pneumonia in slaughtered pigs [Articolo su rivista]
Bonicelli, L.; Trachtman, A. R.; Rosamilia, A.; Liuzzo, G.; Hattab, J.; Alcaraz, E. M.; Del Negro, E.; Vincenzi, S.; Dondona, A. C.; Calderara, S.; Marruchella, G.
abstract

The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time‐consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI‐based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high‐throughput slaughterhouses. The present study aims to develop an AI‐based method capable of recognizing and quantifying enzootic pneumonia‐like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI‐based method proposed herein could properly identify and score enzootic pneumonia‐like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.

2020 - Predicting WNV circulation in Italy using earth observation data and extreme gradient boosting model [Articolo su rivista]
Candeloro, L.; Ippoliti, C.; Iapaolo, F.; Monaco, F.; Morelli, D.; Cuccu, R.; Fronte, P.; Calderara, S.; Vincenzi, S.; Porrello, A.; D'Alterio, N.; Calistri, P.; Conte, A.
abstract

West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV.

2019 - Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs [Relazione in Atti di Convegno]
Vincenzi, Stefano; Porrello, Angelo; Buzzega, Pietro; Conte, Annamaria; Ippoliti, Carla; Candeloro, Luca; Di Lorenzo, Alessio; Capobianco Dondona, Andrea; Calderara, Simone
abstract

Nowadays, Vector-Borne Diseases (VBDs) raise a severe threat for public health, accounting for a considerable amount of human illnesses. Recently, several surveillance plans have been put in place for limiting the spread of such diseases, typically involving on-field measurements. Such a systematic and effective plan still misses, due to the high costs and efforts required for implementing it. Ideally, any attempt in this field should consider the triangle vectors-host-pathogen, which is strictly linked to the environmental and climatic conditions. In this paper, we exploit satellite imagery from Sentinel-2 mission, as we believe they encode the environmental factors responsible for the vector's spread. Our analysis - conducted in a data-driver fashion - couples spectral images with ground-truth information on the abundance of Culicoides imicola. In this respect, we frame our task as a binary classification problem, underpinning Convolutional Neural Networks (CNNs) as being able to learn useful representation from multi-band images. Additionally, we provide a multi-instance variant, aimed at extracting temporal patterns from a short sequence of spectral images. Experiments show promising results, providing the foundations for novel supportive tools, which could depict where surveillance and prevention measures could be prioritized.