The color out of space: learning self-supervised representations for Earth Observation imagery

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

Spotting Insects from Satellites: Modeling the Presence of Culicoides Imicola Through Deep CNNs

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

Evaluating the impact of hydrometeorological conditions on E. coli concentration in farmed mussels and clams: experience in Central Italy.

Abstract Highly populated coastal environments receive large quantities of treated and untreated wastewater from human and industrial sources. Bivalve molluscs accumulate and retain contaminants, and their analysis provides evidence of past contamination. Rivers and precipitation are major routes of bacteriological pollution from surface or sub-surface runoff flowing into coastal areas. However, relationships between runoff, precipitation, and bacterial contamination are site-specific and dependent on the physiographical characteristics of each catchment. In this work, we evaluated the influence of precipitation and river discharge on molluscs' Escherichia coli concentrations at three sites in Central Italy, aiming at quantifying how hydrometeorological conditions affect bacteriological contamination of selected bivalve production areas. Rank-order correlation analysis indicated a stronger association between E. coli concentrations and the modelled Pescara River discharge maxima (r = 0.69) than between E. coli concentration and rainfall maxima (r = 0.35). Discharge peaks from the Pescara River caused an increase in E. coli concentration in bivalves in 87% of cases, provided that the runoff peak occurred 1–6 days prior to the sampling date. Precipitation in coastal area was linked to almost 60% of cases of E. coli high concentrations and may enhance bacterial transportation offshore, when associated with a larger-scale weather system, which causes overflow occurrence

Dioxins levels in breast milk of women living in Caserta and Naples : Assessment of environmental risk factors

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Chapter Sentinel-2 e campionamenti in situ per il monitoraggio delle acque marine dell’Abruzzo: primi risultati

In this study, the estimate of chlorophyll "a" and the dispersion of sediment in the sea, calculated from Sentinel-2, was compared with real data acquired in situ by a multiparametric probe, along the Abruzzo coast. The ultimate goal is to optimize parameters and algorithms to be able to derive concentration maps of chlorophyll and suspended solids from satellite, taking advantage of the high time frequency and high spatial resolution of the detections. This information is of particular relevance for aquaculture activities, for monitoring water quality and for analyzing sedimentary processes

Outbreak of unusualSalmonella entericaserovar Typhimurium monophasic variant 1,4 [5],12:i:-, Italy, June 2013 to September 2014

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Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Running wild, running free : capturing, harnessing and disseminating knowledge flows in support of animal health

Within the project “Cooperation for the implementation of a bluetongue surveillance network in the Balkan area” a web site was developed to provide East European Veterinary Services with an effective tool for data management, analysis and exchange of information on bluetongue, an infectious, arthropod-borne disease of ruminants. The site was designed and implemented by the Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise (Italy) in collaboration with the Joint Research Center of the European Commission (Ispra, Italy). Following new needs of veterinary services and the evolution of the disease, the site was structurally modified using different GIS technologies for the system optimization. Furthermore, geographical data and relevant attributes were organized in a sole Information System (IS) integrated with a relational geographic database and a new function allowing to retrieve information on the spread of the vector causing the disease. The Geographic Information System is based on ESRI products. In particular, an ArcSde was used to connect to Oracle 8.i database while Java and VB script procedures were applied to prepare Asp and Html pages in ArcIms. A multi-user access was implemented, by activating different working sessions, in order to allow a simultaneous geographical data query and map display to different users. Features in the maps displayed may correspond to a polygon (representing the administrative boundaries in which the event of interest occurred) or to a point (farms where data relevant to the event of interest were collected). The querying system allows one to select one or more polygons or points present on the map and to retrieve by the spatial query all the relevant information on the epidemiological status in alphanumerical form; at the same time the ArcIms server shows on the map the selected territory or farms. By linking to the reference database, the alphanumerical database of any country, present in the table shown, can be accessed (Administrative Boundaries) and new data can be entered directly on-line.Poster presented at the 5th International Conference of Animal Health Information Specialists, 4-7 July 2005, Onderstepoort, South AfricaL. Savini, Carla Ippoliti, Sandro Pelini, Annamaria Conte [and] Paolo Calistrihttp://www.library.up.ac.za/vet/icahi