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

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

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

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

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

This paper is about the geometric and radiometric consistency of diverse and overlapping datasets acquired with the Parrot Sequoia camera. The multispectral imagery datasets were acquired above agricultural fields in Northern Italy and radiometric calibration images were taken before each flight. Processing was performed with the Pix4Dmapper suite following a single-block approach: images acquired in different flight missions were processed in as many projects, where different block orientation strategies were adopted and compared. Results were assessed in terms of geometric and radiometric consistency in the overlapping areas. The geometric consistency was evaluated in terms of point cloud distance using iterative closest point (ICP), while the radiometric consistency was analyzed by computing the differences between the reflectance maps and vegetation indices produced according to adopted processing strategies. For normalized difference vegetation index (NDVI), a comparison with Sentinel-2 was also made. This paper will present results obtained for two (out of several) overlapped blocks. The geometric consistency is good (root mean square error (RMSE) in the order of 0.1 m), except for when direct georeferencing is considered. Radiometric consistency instead presents larger problems, especially in some bands and in vegetation indices that have differences above 20%. The comparison with Sentinel-2 products shows a general overestimation of Sequoia data but with similar spatial variations (Pearson’s correlation coefficient of about 0.7, p-value < 2.2 × 10−16)

Standardization of figures and assessment procedures for DTM verticalaccuracy

Digital Terrain Models (DTMs) are widely used in many sectors. They play a key role in hydrological risk prevention, risk mitigation and numeric simulations. This paper deals with two questions: (i) when it is stated that a DTM has a given vertical accuracy, is this assertion univocal? (ii) when DTM vertical accuracy is assessed by means of checkpoints, does their location influence results? First, the paper illustrates that two vertical accuracy definitions are conceivable: Vertical Accuracy at the Nodes (VAN, the average vertical distance between the model and the terrain, evaluated at the DTM's nodes) and Vertical Accuracy at the interpolated Points (VAP, in which the vertical distance is evaluated at the generic points). These two quantities are not coincident and, when they are calculated for the same DTM, different numeric values are reached. Unfortunately, the two quantities are often interchanged, but this is misleading. Second, the paper shows a simulated example of a DTM vertical accuracy assessment, highlighting that the checkpoints’ location plays a key role: when checkpoints coincide with the DTM nodes, VAN is estimated; when checkpoints are randomly located, VAP is estimated, instead. Third, an in-depth, theoretical characterization of the two considered quantities is performed, based on symbolic computation, and suitable standardization coefficients are proposed. Finally, our discussion has a well-defined frame: it doesn't deal with all the items of the DTM vertical accuracy budget, which would require a much longer essay, but only with one, usually called fundamental vertical accuracy