MVP OSM: a tool to identify areas of high quality contributor activity in OpenStreetMap

OpenStreetMap’s success continues to grow and contributions are not limited to the collection of spatial data using GPS (Global Positioning System) equipment. A very wide range of software tools developed by, and available to, the OSM community means that at present, anyone can also make a contribution through, for example, tracing aerial imagery, directly importing data, or by adding spatial information retrieved from smartphones. Consequently ‘the map’ has become increasingly rich, but the quality of the data is very often questioned and comes under scrutiny from the GIS and LBS (Location-Based Services) communities. By examining the world map generated from OpenStreetMap, it is relatively easy to identify areas which are more or less well supported in community mapping activities; a very high level of spatial detail in certain areas can indicate the quality of OSM data. MVP OSM is a software tool designed to highlight areas in OpensStreetMap where users (contributors) are dedicated to providing high levels of spatial detail. This usually correlates with the use of a GPS and on-the-ground mapping, or, at the very least, a deep local knowledge of the area and an inherent desire to see it represented in the highest level of detail on OpenStreetMap. The input to MVP OSM is an OSM XML file, which is converted by Python into a file for spatialite (the GIS extension for sqlite). Within spatialite the data is processed to create clusters and using these spatial clusters, the tool can then derive the activity of single or multiple users in that area. Vector layers and heatmaps are generated as output that can be overlaid onto OSM maps. A high level of detail can be considered a good indicator of the quality of OSM data within a given area. The MVP OSM tool hides the details of OSM XML processing, which many researchers find difficult, and processes the data to produce very useful visualizations of contributor activity in any given OSM area

An innovative numerical approach for railway rolling noise forecast

In recent years there has been a growing worldwide development of rail transport, mainly due to technological innovations both on armaments and on rail vehicles. Such technological issue focused almost parallel on two main fronts: on one hand the performance enhancement and on the other side the internal comfort. This technology advancement has been driven mainly by the need to move goods and passengers over long distances in a short time, making it the safest transportation system in the world thanks also to the latest monitoring systems, of which European Community is undoubtedly one of the major leaders. The passenger transport has introduced problems related to comfort: traveling so fast is the main goal so long as it is comfortable and safe. One of the requirements that mostly turned out to be significant and sometimes more difficult to satisfy is that regarding acoustic comfort and environmental impact. As known, the regulations become with the passage of time more and more stringent, and every company that wants to operate in this area is required to respect them. The acoustic comfort improvement implies the intervention as much as possible focused on noise sources, which in this case are constituted by: electric motor, pantograph, wheel-rail contact. In such research framework, the authors focused on determination of a simple, but at the same time reliable, method for radiated sound power assessment in the wheel-rail contact due to combined wheel-rail roughness in order to reduce the environmental impact of this type of transmission system. Targeted analysis were implemented in an efficient numerical investigation in MSC NASTRAN® and ACTRAN® environments providing the necessary vibro-acoustic parameters as input data for the further definition of the wheel-rail interaction force by a MATLAB® customized tool, once known the roughness profile

Acoustic performance assessment of innovative blankets for aeronautical applications

Polyurethane blankets are increasingly used for many aeronautical NVH applications. These foams, generally available in various thickness and density, are great sound absorber, therefore suitable in the aircraft interior. These foams are used as replacement to traditional combination of mineral wools / rock wool along with perforated panels, which require labor and also health hazardous. Polyurethane foams are generally available in various densities and thickness. The acoustic performance of sound absorbing poroelastic materials is characterized by intrinsic physical parameters like flow resistivity, and absorption coefficient. This paper presents a detailed discussion on measurement of flow resistivity as well as acoustic absorption coefficient of PU foam samples. Such numerical database of examined samples has been then validated through other laboratories activities, which shows the good accuracy of the methodology implemented within

Remote Sensing and Deep Learning to Understand Noisy OpenStreetMap

The OpenStreetMap (OSM) project is an open-source, community-based, user-generated street map/data service. It is the most popular project within the state of the art for crowdsourcing. Although geometrical features and tags of annotations in OSM are usually precise (particularly in metropolitan areas), there are instances where volunteer mapping is inaccurate. Despite the appeal of using OSM semantic information with remote sensing images, to train deep learning models, the crowdsourced data quality is inconsistent. High-resolution remote sensing image segmentation is a mature application in many fields, such as urban planning, updated mapping, city sensing, and others. Typically, supervised methods trained with annotated data may learn to anticipate the object location, but misclassification may occur due to noise in training data. This article combines Very High Resolution (VHR) remote sensing data with computer vision methods to deal with noisy OSM. This work deals with OSM misalignment ambiguity (positional inaccuracy) concerning satellite imagery and uses a Convolutional Neural Network (CNN) approach to detect missing buildings in OSM. We propose a translating method to align the OSM vector data with the satellite data. This strategy increases the correlation between the imagery and the building vector data to reduce the noise in OSM data. A series of experiments demonstrate that our approach plays a significant role in (1) resolving the misalignment issue, (2) instance-semantic segmentation of buildings with missing building information in OSM (never labeled or constructed in between image acquisitions), and (3) change detection mapping. The good results of precision (0.96) and recall (0.96) demonstrate the viability of high-resolution satellite imagery and OSM for building detection/change detection using a deep learning approach

30Cappa - The “Christmas” decree in kilometres

Following the COVID-19 pandemic emergency, in mid-December 2020 the Italian government introducedtravel restrictions during the Christmas holidays. In the implemented policies there was an exception:citizens of a municipality of up to 5,000 citizens can move within an area of 30 kilometers from their respectiveborders. This policy was used again in the following months to manage travel during the pandemic.30Cappa is a data visualization created by three civic hackers to give citizens the opportunity to understandthis policy (”cappa” is the pronunciation of the letter ”k” in Italian and means ”km”). The project consistsof a website where it is possible to receive information on the matter and the cartographic representationof the municipalities that correspond to the exception. The site has reached 350,000 unique visitors in twomonths and there has been a lot of talk about it in the media. This work highlights how the transformationof a policy into a tangible product such as a map created with the open data available, becomes an effectivetool to guide citizens and also to review the policies themselve

L'importante ruolo degli open data nelle emergenze

The important role of open data in emergencies During emergencies, the geographic information plays a very important and delicate role becoming the main tool to understand what happened and to take decisions. The data therefore will play a crucial role. The availability of data as soon as possible, with the guarantee that they are correct, is a non-trivial aspect

Open data as a public asset

The disclosure of data is often accompanied by many promises from an open government standpoint that use catchwords such as transparency, participation and engagement, not to mention innovation, viewing such data as a raw material that could be used by old and new companies to create and improve services. Although these promises effectively represent the potential of open data, in actual fact, they prove very difficult to accomplish. The delays, or rather the hurdles, that stand in the way of these data’s use are multi-faceted: cultural resistance to sharing data, legal constraints and issues with creating sustainable updating processes. By retracing the steps of open data, this article highlights these issues, proposes courses of action and suggests considering open data as a common good: a resource shared by all the members of a community and from which everyone can benefi

AN INNOVATIVE NUMERICAL APPROACH FOR TRAIN PASS-BY NOISE FORECASTING

This paper deals with an engineering method for the prediction of vehicle pass-by noise based on a FEM/ BEM exterior acoustic calculation in the frequency domain. The researchers simulate, in a virtual environment, the experimental outdoor pass-by noise measurement. The simulated pass-by noise campaign is synthesized from multiple acoustic transfer functions between a line of virtual microphones located 7.5m on the side of the vehicle and each noise source. A numerical FEM/BEM train bogie acoustic model has been created within the MSC ACTRAN commercial softwares. Wheel-rail rolling noise, engine and powertrain noise acoustic source have been implemented and posi-tioned inside the FEM and BEM model to demonstrate the validity of the proposed methods. The contribu-tion from noise sources, expressed both in terms of sound pressure level and overall value, to the pass-by noise were evaluated up to 5 kHz. The virtual pass-by-noise assessment has been then validated by experi-mental measurement of the complete four coach’s train with respect to different speed regimes

Collaborative mapping response to disasters through OpenStreetMap: the case of the 2016 Italian earthquake

Digital humanitarians represent the current generation of volunteers providing timely contributions in the form of digital map data in the aftermath of natural disasters. Starting from the tragic 2010 earthquake in Haiti and thanks to the success of the OpenStreetMap (OSM) project, the presence and coordination of these volunteers have grown incredibly over the past years. This work investigates the dynamics of the mapping process and the nature of the OSM volunteers who contributed map data after the 2016 earthquake in Central Italy. The analyses show that the existing OSM users were the majority of those contributing to the mapping activity, with less edits performed by new users. The collaborative mapping process was efficiently coordinated through a dedicated platform and the area hit by the earthquake was significantly edited in OSM after the disaster

A New Bloody Pulp Selection of Myrobalan (Prunus cerasifera L.): Pomological Traits, Chemical Composition, and Nutraceutical Properties

A new accession of myrobalan (Prunus cerasifera L.) from Sicily (Italy) was studied for the first time for its chemical and nutraceutical properties. A description of the main morphological and pomological traits was created as a tool for characterization for consumers. For this purpose, three different extracts of fresh myrobalan fruits were subjected to different analyses, including the evaluation of total phenol (TPC), flavonoid (TFC), and anthocyanin (TAC) contents. The extracts exhibited a TPC in the range 34.52-97.63 mg gallic acid equivalent (GAE)/100 g fresh weight (FW), a TFC of 0.23-0.96 mg quercetin equivalent (QE)/100 g FW, and a TAC of 20.24-55.33 cyanidine-3-O-glucoside/100 g FW. LC-HRMS analysis evidenced that the compounds mainly belong to the flavonols, flavan-3-ols, proanthocyanidins, anthocyanins, hydroxycinnamic acid derivatives, and organic acids classes. A multitarget approach was used to assess the antioxidant properties by using FRAP, ABTS, DPPH, and β-carotene bleaching tests. Moreover, the myrobalan fruit extracts were tested as inhibitors of the key enzymes related to obesity and metabolic syndrome (α-glucosidase, α-amylase, and lipase). All extracts exhibited an ABTS radical scavenging activity that was higher than the positive control BHT (IC50 value in the range 1.19-2.97 μg/mL). Moreover, all extracts showed iron-reducing activity, with a potency similar to that of BHT (53.01-64.90 vs 3.26 μM Fe(II)/g). The PF extract exhibited a promising lipase inhibitory effect (IC50 value of 29.61 μg/mL)