WEIGHTED ICP POINT CLOUDS REGISTRATION BY SEGMENTATION BASED ON EIGENFEATURES CLUSTERING

Abstract. Dense point clouds can be nowadays considered the main product of UAV (Unmanned Aerial Vehicle) photogrammetric processing and clouds registration is still a key aspect in case of blocks acquired apart. In the paper some overlapping datasets, acquired with a multispectral Parrot Sequoia camera above some rice fields, are analysed in a single block approach. Since the sensors is equipped with a navigation-grade sensor, the georeferencing information is affected by large errors and the so obtained dense point clouds are significantly far apart: to register them the Iterative Closes Point (ICP) technique is applied. ICP convergence is fundamentally based on the correct selection of the points to be coupled, and the paper proposes an innovative procedure in which a double density points subset is selected in relation to terrain characteristics. This approach reduces the complexity of the calculation and avoids that flat terrain parts, where most of the original points, are de-facto overweighed. Starting from the original dense cloud, eigenfeatures are extracted for each point and clustering is then performed to group them in two classes connected to terrain geometry, flat terrain or not; two metrics are adopted and compared for k-means clustering, Euclidean and City Block. Segmentation results are evaluated visually and by comparison with manually performed classification; ICP are then performed and the quality of registration is assessed too. The presented results show how the proposed procedure seem capable to register clouds even far apart with a good overall accuracy

Filtering and Mapping Public Health Data with an Innovative Kriging Approach, Accounting for Single Observation Variance

AbstractThe main scope of the paper is performing appropriate kriging interpolation of the diabetes prevalence data coming from the Pavia (Italy) Local Health Care Agency (ASL). The original dataset is analyzed, the Bayesian regularization is evaluated, which is applied by other authors and finally prevalence data are simulated by means of random fields, in order to tune and evaluate kriging interpolation

Dynamic Assessment of Personal Exposure to Air Pollution for Everyone: a Smartphone-Based Approach

Abstract. In Epidemiology, exposure assessment is the process of measuring or estimating the intensity of human exposures to an environmental agent such as air pollution. Healthcare agencies typically take into consideration yearly averaged pollution values and apply them to all citizens, in risk models. However distinct parts of cities can have significantly different levels of pollution and individual habits can influence exposure, too. Consequently, in epidemiology and public health, there is an increasing interest for personal exposure assessment, i.e. the capability of measuring the exposure of individuals. Within the EU H2020 PULSE project, an innovative mechanism for the individual and dynamic assessment of exposure to air pollution has been implemented. The present paper illustrates its technological and scientific components. The system has already been deployed to several pilot cities of the project and Pavia, Italy, has been the first one. In that city several hundreds of tracks have already been acquired and processed. Therefore, the paper thoroughly illustrates the assessment procedure with examples

HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas

Public Health Observatories: a learning community model to foster knowledge transfer for sustainable cities

[EN] A Public Health Observatory (PHO) is a platform to provide “health intelligence” as a service for a specific population. The World Health Organization (WHO) identifies the primary purposes of PHOs as “monitoring health situations and trends, including assessing progress toward agreed-upon health-related targets; producing and sharing evidence; and, supporting the use of such evidence for policy and decision making” For the purposes of the PULSE project, create an observatory to function as a unique point of access to the PULSE technology for people both inside and outside the project consortium.Specifically, we create a platform for e-learning and knowledge sharing that it can be easily navigated by lay persons that are interested in learning about or participating in the PULSE project. We targeted specifically policymakers, clinicians, as well as leaders and citizens in other cities. As a concept, it reflects the principles participation, sustainability, and collaboration across sectors and levels of government The Observatory leverages on the Health in All Policies (HiAP) framework. HiAP is a cross-sectoral approach to public policy that systematically takes into account the health implications of decisions, seeks synergies, and avoids harmful health impacts in order to improve population health and health equity.PULSE project has been founded by the European Union’s Horizon 2020 research and innovation programme, and it is documented in the grant agreement No 727816. Specifically. PULSE has been founded under the call H2020-EU-3.1.5. in the topic SCIPM-18-2016-Big Data supporting Public Health policies. More information on http://www.project-pulse.euVito, D.; Ottaviano, M.; Cabrera, MF.; Teriús Padrón, JG.; Casella, V.; Bellazzi, R. (2020). Public Health Observatories: a learning community model to foster knowledge transfer for sustainable cities. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):1383-1390. https://doi.org/10.4995/HEAd20.2020.11285OCS1383139030-05-202

CUSTOMIZED WEBGIS SOLUTIONS FOR EXPOSOMICS

Abstract. Exposomics is a science aiming at quantifying the effects on human health of all the factors influencing it, but genetic ones. They include environment, food, mobility habits and cultural factors. The percentage of the world's population living in the urban areas is projected to increase in the next decades. Rising industrialization, urbanization and heterogeneity are leading to new challenges for public health and quality of life in the population. The prevalence of conditions such as asthma and cardiovascular diseases is increasing due to a change in lifestyle and air quality. This enlightens the necessity of targeted interventions to increase citizens' quality of life and decrease their health risks. Within the EU H2020 PULSE project, a multi-technological system to assist the population in the prevention and treatment of asthma and type 2 diabetes has been developed. The system created in PULSE features several parts, such as a personal App for the citizens, a set of air quality sensors, a WebGIS and dashboards for the public health operators. Citizens are directly involved in an exchange paradigm in which they send their own data and receive feedbacks and suggestions about their health in return. The WebGIS is a very distinguishing element of the PULSE technology and the paper illustrates its main functionalities focusing on the distinguishing and innovative features developed

Rectal bleeding by Dieulafoy-like lesion: successful endoscopic treatment

A 81-year old woman affected by chronic renal failure, non insulin-dependent diabetes mellitus (NIDM) and hypertension, had an severe anemia massive hematochezia. The colonoscopy could not localize the bleeding site except some blood spots in the rectum. The patient was readmitted after 1 month with hypovolemic shock by massive hematochezia and required several blood transfusions. The endoscopic examination showed an important arterial bleeding treated successfully with epinephrine and bipolar elettro-coagulation (BICAP). We suggested that the patient presented a Dieulafoy-like lesion; this is an uncommon gastrointestinal cause of bleeding due to a defect of a submucosal artery without evidence of atherosclerosis or vasculitis. Both chronic renal failure and age could be considered as predisponent factors in this patient. Hematochezia is the most important sign and is often complicated by haemorrhagic shock. The diagnosis was delayed due to the difficulty in localizing the bleeding site; moreover, the patient needed several blood transfusions. The arteriographic diagnosis associated to endoscopic treatment by epinephrine and BICAP enabled a successful therapy

Spatial Enablement to Support Environmental, Demographic, Socioeconomics, and Health Data Integration and Analysis for Big Cities: A Case Study With Asthma Hospitalizations in New York City

The percentage of the world's population living in urban areas is projected to increase in the next decades. Big cities are heterogeneous environments in which socioeconomic and environmental differences among the neighborhoods are often very pronounced. Each individual, during his/her life, is constantly subject to a mix of exposures that have an effect on their phenotype but are frequently difficult to identify, especially in an urban environment. Studying how the combination of environmental and socioeconomic factors which the population is exposed to influences pathological outcomes can help transforming public health from a reactive to a predictive system. Thanks to the application of state-of-the-art spatially enabled methods, patients can be stratified according to their characteristics and the geographical context they live in, optimizing healthcare processes and the reducing its costs. Some public health studies focusing specifically on urban areas have been conducted, but they usually consider a coarse spatial subdivision, as a consequence of scarce availability of well-integrated data regarding health and environmental exposure at a sufficient level of granularity to enable meaningful statistical analyses. In this paper, we present an application of highly fine-grained spatial resolution methods to New York City data. We investigated the link between asthma hospitalizations and a combination of air pollution and other environmental and socioeconomic factors. We first performed an explorative analysis using spatial clustering methods that shows that asthma is related to numerous factors whose level of influence varies considerably among neighborhoods. We then performed a Geographically Weighted Regression with different covariates and determined which environmental and socioeconomic factors can predict hospitalizations and how they vary throughout the city. These methods showed to be promising both for visualization and analysis of demographic and epidemiological urban dynamics, that can be used to organize targeted intervention and treatment policies to address the single citizens considering the factors he/she is exposed to. We found a link between asthma and several factors such as PM2.5, age, health insurance coverage, race, poverty, obesity, industrial areas, and recycling. This study has been conducted within the PULSE project, funded by the European Commission, briefly presented in this paper

Transfer Learning for Urban Landscape Clustering and Correlation with Health Indexes

Within the EU-funded Pulse project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches to jointly analyze maps and geospatial information with health care data and air pollution measurements. In this paper we describe a component of such platform, designed to couple deep learning analysis of geospatial images of cities and some healthcare and behavioral indexes collected by the 500 cities US project, showing that, in New York City, urban landscape significantly correlates with the access to healthcare services

HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas