Official Statistics, Building Censuses, and OpenStreetMap Completeness in Italy

The present study provides a simplified framework verifying the degree of coverage and completeness of settlement maps derived from the OpenStreetMap (OSM) database at the national scale, with a possible use in official statistics. Measuring the completeness of the objects (i.e., buildings) derived from OpenStreetMap database supports its potential use in building/population censuses and other diachronic surveys, as well as administrative sources such as the register of building permits and land-use cadasters. A series of measurements at different scales are proposed and tested for Italy, in line with earlier studies. While recognizing the potential of the OpenStreetMap database for official statistics, the present work underlines the urgent need of an additional (spatially explicit) analysis overcoming the data heterogeneity and sub-optimal coverage of the OSM information source

Non-equilibrium Green's functions in density functional tight binding: method and applications

We present a detailed description of the implementation of the non-equilibrium Green's function (NEGF) technique on the density-functional-based tight-binding (gDFTB) simulation tool. This approach can be used to compute electronic transport in organic and inorganic molecular-scale devices. The DFTB tight-binding formulation gives an efficient computational tool that is able to handle a large number of atoms. NEGFs are used to compute the electronic density self-consistently with the open-boundary conditions naturally encountered in quantum transport problems and the boundary conditions imposed by the potentials at the contacts. The efficient block-iterative algorithm used to compute the Green's functions is illustrated. The Hartree potential of the density-functional Hamiltonian is obtained by solving the three-dimensional Poisson equation. A scheme to treat geometrically complex boundary conditions is discussed, including the possibility of including multiterminal calculations

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

The accurate automatic volcanic cloud detection by means of satellite data is a challenging task and of great concern for both scientific community and stakeholder due to the well-known issues generated by a strong eruption event in relation to aviation safety and health impact. In this context, machine learning techniques applied to recent spaceborne sensors acquired data have shown promising results in the last years. This work focuses on the application of a neural network based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. The classification of the clouds and of the other surfaces composing the scene is also carried out. The neural network has been trained with MODIS (MODerate resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallaj&ouml;kull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the events comparable latitudes foster the robustness of the approach, which allows overcoming the lack in SLSTR products collected in previous mid-high latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared with RGB visual inspection and BTD (Brightness Temperature Difference) procedure. Moreover, the comparison between the ash cloud obtained by neural network and a plume mask manually generated for the specific SLSTR considered images, shows significant agreement. Thus, the proposed approach allows an automatic image classification during eruption events, which it is also considerably faster than time-consuming manually algorithms (e.g. find the best BTD product-specific threshold). Furthermore, the whole image classification indicates an overall reliability of the algorithm, in particular for meteo-clouds recognition and discrimination from volcanic clouds. Finally, the results show that the NN developed for the SLSTR nadir view is able to properly classify also the SLSTR oblique view images.</p

Differences in Multitask Resource Reallocation After Change in Task Values

International audienceObjective The objective was to characterize multitask resource reallocation strategies when managing subtasks with various assigned values.Background When solving a resource conflict in multitasking, Salvucci and Taatgen predict a globally rational strategy will be followed that favors the most urgent subtask and optimizes global performance. However, Katidioti and Taatgen identified a locally rational strategy that optimizes only a subcomponent of the whole task, leading to detrimental consequences on global performance. Moreover, the question remains open whether expertise would have an impact on the choice of the strategy.Method We adopted a multitask environment used for pilot selection with a change in emphasis on two out of four subtasks while all subtasks had to be maintained over a minimum performance. A laboratory eye-tracking study contrasted 20 recently selected pilot students considered as experienced with this task and 15 university students considered as novices.Results When two subtasks were emphasized, novices focused their resources particularly on one high-value subtask and failed to prevent both low-value subtasks falling below minimum performance. On the contrary, experienced people delayed the processing of one low-value subtask but managed to optimize global performance.Conclusion In a multitasking environment where some subtasks are emphasized, novices follow a locally rational strategy whereas experienced participants follow a globally rational strategy.Application During complex training, trainees are only able to adjust their resource allocation strategy to subtask emphasis changes once they are familiar with the multitasking environment