Novel neural network-based algorithms for urban classification and change detection from satellite imagery

L`attività umana sta cambiando radicalmente l`ecosistema ambientale, unito anche alla rapida espansione demografica dei sistemi urbani. Benche` queste aree rappresentano solo una minima frazione della Terra, il loro impatto sulla richiesta di energia, cibo, acqua e materiali primi, e` enorme. Per cui, una informazione accurata e tempestiva risulta essere essenziale per gli enti di protezione civile in caso, ad esempio, di catastrofi ambientali. Negli ultimi anni il forte sviluppo di sistemi satellitari, sia dal punto di vista della risoluzione spaziale che di quella radiometrica e temporale, ha permesso una sempre piu` accurato monitoraggio della Terra, sia con sistemi ottici che con quelli RADAR. Ad ogni modo, una piu` alta risoluzione (sia spaziale, che spettrale o temporale) presenta tanti vantaggi e miglioramenti quanti svantaggi e limitazioni. In questa tesi sono discussi in dettaglio i diversi aspetti e tecniche per la classificazione e monitoraggio dei cambiamenti di aree urbane, utilizzando sia sistemi ottici che RADAR. Particolare enfasi e` data alla teoria ed all`uso di reti neurali.Human activity dominates the Earth's ecosystems with structural modifications. The rapid population growth over recent decades and the concentration of this population in and around urban areas have significantly impacted the environment. Although urban areas represent a small fraction of the land surface, they affect large areas due to the magnitude of the associated energy, food, water, and raw material demands. Reliable information in populated areas is essential for urban planning and strategic decision making, such as civil protection departments in cases of emergency. Remote sensing is increasingly being used as a timely and cost-effective source of information in a wide number of applications, from environment monitoring to location-aware systems. However, mapping human settlements represents one of the most challenging areas for the remote sensing community due to its high spatial and spectral diversity. From the physical composition point of view, several different materials can be used for the same man-made element (for example, building roofs can be made of clay tiles, metal, asphalt, concrete, plastic, grass or stones). On the other hand, the same material can be used for different purposes (for example, concrete can be found in paved roads or building roofs). Moreover, urban areas are often made up of materials present in the surrounding region, making them indistinguishable from the natural or agricultural areas (examples can be unpaved roads and bare soil, clay tiles and bare soil, or parks and vegetated open spaces) [1]. During the last two decades, significant progress has been made in developing and launching satellites with instruments, in both the optical/infrared and microwave regions of the spectra, well suited for Earth observation with an increasingly finer spatial, spectral and temporal resolution. Fine spatial sensors with metric or sub-metric resolution allow the detection of small-scale objects, such as elements of residential housing, commercial buildings, transportation systems and utilities. Multi-spectral and hyper-spectral remote sensing systems provide additional discriminative features for classes that are spectrally similar, due to their higher spectral resolution. The temporal component, integrated with the spectral and spatial dimensions, provides essential information, for example on vegetation dynamics. Moreover, the delineation of temporal homogeneous patches reduces the effect of local spatial heterogeneity that often masks larger spatial patterns. Nevertheless, higher resolution (spatial, spectral or temporal) imagery comes with limits and challenges that equal the advantages and improvements, and this is valid for both optical and synthetic aperture radar data [2]. This thesis addresses the different aspects of mapping and change detection of human settlements, discussing the main issues related to the use of optical and synthetic aperture radar data. Novel approaches and techniques are proposed and critically discussed to cope with the challenges of urban areas, including data fusion, image information mining, and active learning. The chapters are subdivided into three main parts. Part I addresses the theoretical aspects of neural networks, including their different architectures, design, and training. The proposed neural networks-based algorithms, their applications to classification and change detection problems, and the experimental results are described in Part II and Part III

Use of dermal-fat grafts in the post-oncological reconstructive surgery of atrophies in the zygomatic region: clinical evaluations in the patients undergone to previous radiation therapy

INTRODUCTION: Grafting of autologous adipose tissue can be recommended in some cases of facial plastic surgery. Rhabdomyosarcoma is a type of cancer that can also affect the orbit. Enucleation of the eye can cause atrophy of the corresponding hemiface and decreased orbital growth. CASE REPORT: We report a case of a female patient with a medical history of surgical enucleation of the right eyeball, who had received rhabdomyosarcoma radiation therapy in her youth. The patient presented with a depression in the right zygomatic region. We took a dermal-fat flap from the abdominal region, which had been previously treated. RESULTS: The surgical outcome, 48 hours, and much clearly 31 days after the surgery, revealed that the right zygomatic region had returned to its proper anatomical shape, although there were still signs of postoperative edema. DISCUSSION: Very damaged tissues, like those exposed to radiation therapy, are generally not suitable for grafting of adipose tissue. CONCLUSIONS: In the described case, we achieved a technically and aesthetically satisfying result despite the patient's medical history involving several perplexities about the use of autologous dermal-fat tissues, because of prior radiation therapy exposure. The clinical case shows that even a region exposed to radiation therapy can be a valid receiving bed for dermal-fat grafting

FGF-2b and h-PL transform duct and non-endocrine human pancreatic cells into endocrine insulin secreting cells by modulating differentiating genes

Background: Diabetes mellitus (DM) is a multifactorial disease orphan of a cure. Regenerative medicine has been proposed as novel strategy for DM therapy. Human fibroblast growth factor (FGF)-2b controls β-cell clusters via autocrine action, and human placental lactogen (hPL)-A increases functional β-cells. We hypothesized whether FGF-2b/hPL-A treatment induces β-cell differentiation from ductal/non-endocrine precursor(s) by modulating specific genes expression. Methods: Human pancreatic ductal-cells (PANC-1) and non-endocrine pancreatic cells were treated with FGF-2b plus hPL-A at 500 ng/mL. Cytofluorimetry and Immunofluorescence have been performed to detect expression of endocrine, ductal and acinar markers. Bromodeoxyuridine incorporation and annexin-V quantified cells proliferation and apoptosis. Insulin secretion was assessed by RIA kit, and electron microscopy analyzed islet-like clusters. Results: Increase in PANC-1 duct cells de-differentiation into islet-like aggregates was observed after FGF-2b/hPL-A treatment showing ultrastructure typical of islets-aggregates. These clusters, after stimulation with FGF-2b/hPL-A, had significant (p < 0.05) increase in insulin, C-peptide, pancreatic and duodenal homeobox 1 (PDX-1), Nkx2.2, Nkx6.1, somatostatin, glucagon, and glucose transporter 2 (Glut-2), compared with control cells. Markers of PANC-1 (Cytokeratin-19, MUC-1, CA19-9) were decreased (p < 0.05). These aggregates after treatment with FGF-2b/hPL-A significantly reduced levels of apoptosis. Conclusions: FGF-2b and hPL-A are promising candidates for regenerative therapy in DM by inducing de-differentiation of stem cells modulating pivotal endocrine genes

BioNNA: the Biodiversity National Network of Albania

Recently, the Albanian Government started the process to join the European Union. This process also involves matching the EU parameters in protecting its biodiversity. In order to support the Albanian authorities, the Italian Ministry of Foreign Affairs, General Directorate for Development Cooperation (DGCS) and the International Union for Conservation of Nature (IUCN) joined efforts in the project “Institutional Support to the Albanian Ministry of Environment, Forest and Water Administration for Sustainable Biodiversity Conservation and Use in Protected Areas”. This project aims at identifying priority needs in safeguarding ecosystem services and biodiversity conservation. Another project funded by the EU – “Strengthening capacity in National Nature Protection – preparation for Natura 2000 network” – started in 2015 with the aim to raise awareness for assisting local and national Albanian institutions to better exploit the potential of protected areas. One of the main issues encountered during these projects was the need for a national biodiversity data repository. The Biodiversity National Network of Albania (BioNNA) has been created to aggregate occurrence records of plants and animals and aims at becoming the most relevant source of information for biodiversity data as far as Albania is concerned. In this paper, the authors detail structure and data of BioNNA, including the process of data gathering and aggregation, taxonomic coverage, software details and WebGIS development. BioNNA is a milestone on the path towards Albania’s inclusion in the EU and has also a relevant potential social relevance for improving people’s awareness on the importance of biodiversity in the country

Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land

The correction of the atmospheric effects on optical satellite images is essential for quantitative and multitemporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD 90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals

Covid-19 and the role of smoking: the protocol of the multicentric prospective study COSMO-IT (COvid19 and SMOking in ITaly).

The emergency caused by Covid-19 pandemic raised interest in studying lifestyles and comorbidities as important determinants of poor Covid-19 prognosis. Data on tobacco smoking, alcohol consumption and obesity are still limited, while no data are available on the role of e-cigarettes and heated tobacco products (HTP). To clarify the role of tobacco smoking and other lifestyle habits on COVID-19 severity and progression, we designed a longitudinal observational study titled COvid19 and SMOking in ITaly (COSMO-IT). About 30 Italian hospitals in North, Centre and South of Italy joined the study. Its main aims are: 1) to quantify the role of tobacco smoking and smoking cessation on the severity and progression of COVID-19 in hospitalized patients; 2) to compare smoking prevalence and severity of the disease in relation to smoking in hospitalized COVID-19 patients versus patients treated at home; 3) to quantify the association between other lifestyle factors, such as e-cigarette and HTP use, alcohol and obesity and the risk of unfavourable COVID-19 outcomes. Socio-demographic, lifestyle and medical history information will be gathered for around 3000 hospitalized and 700-1000 home-isolated, laboratory-confirmed, COVID-19 patients. Given the current absence of a vaccine against SARS-COV-2 and the lack of a specific treatment for -COVID-19, prevention strategies are of extreme importance. This project, designed to highly contribute to the international scientific debate on the role of avoidable lifestyle habits on COVID-19 severity, will provide valuable epidemiological data in order to support important recommendations to prevent COVID-19 incidence, progression and mortality

A follow-up study of heroin addicts (VEdeTTE2): study design and protocol

BACKGROUND: In Italy, a large cohort study (VEdeTTE1) was conducted between 1998–2001 to evaluate the effectiveness of treatments in reducing mortality and increasing treatment retention among heroin addicts. The follow-up of this cohort (VEdeTTE2) was designed to evaluate the effectiveness of treatments on long-term outcomes, such as rehabilitation and social re-integration. The purpose of this paper is to describe the protocol of the VEdeTTE2 study, and to present the results of the pilot study carried out to assess the feasibility of the study and to improve study procedures. METHODS: The source population for the VEdeTTE2 study was the VEdeTTE1 cohort, from which a sample of 2,200 patients, traced two or more years after enrolment in the cohort, were asked to participate. An interview investigates drug use; overdose; family and social re-integration. Illegal activity are investigated separately in a questionnaire completed by the patient. Patients are also asked to provide a hair sample to test for heroin and cocaine use. Information on treatments and HIV, HBV and HCV morbidity are obtained from clinical records. A pilot phase was planned and carried out on 60 patients. RESULTS: The results of the pilot phase pointed out the validity of the procedures designed to limit attrition: the number of traced subjects was satisfactory (88%). Moreover, the pilot phase was very useful in identifying possible causes of delays and attrition, and flaws in the instruments. Improvements to the procedures and the instruments were subsequently implemented. Sensitivity of the biological test was quite good for heroin (78%) but lower for cocaine (42.3%), highlighting the need to obtain a hair sample from all patients. CONCLUSION: In drug addiction research, studies investigating health status and social re-integration of subjects at long-term follow-up are lacking. The VEdeTTE2 study aims to investigate these outcomes at long-term follow-up. Results of the pilot phase underline the importance of the pilot phase when planning a follow-up study

Novel neural network-based algorithms for urban classification and change detection from satellite imagery

L`attività umana sta cambiando radicalmente l`ecosistema ambientale, unito anche alla rapida espansione demografica dei sistemi urbani. Benche` queste aree rappresentano solo una minima frazione della Terra, il loro impatto sulla richiesta di energia, cibo, acqua e materiali primi, e` enorme. Per cui, una informazione accurata e tempestiva risulta essere essenziale per gli enti di protezione civile in caso, ad esempio, di catastrofi ambientali. Negli ultimi anni il forte sviluppo di sistemi satellitari, sia dal punto di vista della risoluzione spaziale che di quella radiometrica e temporale, ha permesso una sempre piu` accurato monitoraggio della Terra, sia con sistemi ottici che con quelli RADAR. Ad ogni modo, una piu` alta risoluzione (sia spaziale, che spettrale o temporale) presenta tanti vantaggi e miglioramenti quanti svantaggi e limitazioni. In questa tesi sono discussi in dettaglio i diversi aspetti e tecniche per la classificazione e monitoraggio dei cambiamenti di aree urbane, utilizzando sia sistemi ottici che RADAR. Particolare enfasi e` data alla teoria ed all`uso di reti neurali.Human activity dominates the Earth's ecosystems with structural modifications. The rapid population growth over recent decades and the concentration of this population in and around urban areas have significantly impacted the environment. Although urban areas represent a small fraction of the land surface, they affect large areas due to the magnitude of the associated energy, food, water, and raw material demands. Reliable information in populated areas is essential for urban planning and strategic decision making, such as civil protection departments in cases of emergency. Remote sensing is increasingly being used as a timely and cost-effective source of information in a wide number of applications, from environment monitoring to location-aware systems. However, mapping human settlements represents one of the most challenging areas for the remote sensing community due to its high spatial and spectral diversity. From the physical composition point of view, several different materials can be used for the same man-made element (for example, building roofs can be made of clay tiles, metal, asphalt, concrete, plastic, grass or stones). On the other hand, the same material can be used for different purposes (for example, concrete can be found in paved roads or building roofs). Moreover, urban areas are often made up of materials present in the surrounding region, making them indistinguishable from the natural or agricultural areas (examples can be unpaved roads and bare soil, clay tiles and bare soil, or parks and vegetated open spaces) [1]. During the last two decades, significant progress has been made in developing and launching satellites with instruments, in both the optical/infrared and microwave regions of the spectra, well suited for Earth observation with an increasingly finer spatial, spectral and temporal resolution. Fine spatial sensors with metric or sub-metric resolution allow the detection of small-scale objects, such as elements of residential housing, commercial buildings, transportation systems and utilities. Multi-spectral and hyper-spectral remote sensing systems provide additional discriminative features for classes that are spectrally similar, due to their higher spectral resolution. The temporal component, integrated with the spectral and spatial dimensions, provides essential information, for example on vegetation dynamics. Moreover, the delineation of temporal homogeneous patches reduces the effect of local spatial heterogeneity that often masks larger spatial patterns. Nevertheless, higher resolution (spatial, spectral or temporal) imagery comes with limits and challenges that equal the advantages and improvements, and this is valid for both optical and synthetic aperture radar data [2]. This thesis addresses the different aspects of mapping and change detection of human settlements, discussing the main issues related to the use of optical and synthetic aperture radar data. Novel approaches and techniques are proposed and critically discussed to cope with the challenges of urban areas, including data fusion, image information mining, and active learning. The chapters are subdivided into three main parts. Part I addresses the theoretical aspects of neural networks, including their different architectures, design, and training. The proposed neural networks-based algorithms, their applications to classification and change detection problems, and the experimental results are described in Part II and Part III