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In recent years, volunteered-geographic-information (VGI) image data have served as a data source for various geographic applications, attracting researchers to assess the quality of these images. However, these applications and quality assessments are generally focused on images associated with geolocation through textual annotations, which is only part of valid images to them. In this paper, we explore the distribution pattern for most relevant VGI images of specific landmarks to extend the current quality analysis, and to provide guidance for improving the data-retrieval process of geographic applications. Distribution is explored in terms of two aspects, namely, semantic distribution and spatial distribution. In this paper, the term semantic distribution is used to describe the matching of building-image tags and content with each other. There are three kinds of images (semantic-relevant and content-relevant, semantic-relevant but content-irrelevant, and semantic-irrelevant but content-relevant). Spatial distribution shows how relevant images are distributed around a landmark. The process of this work can be divided into three parts: data filtering, retrieval of relevant landmark images, and distribution analysis. For semantic distribution, statistical results show that an average of 60% of images tagged with the building&rsquo, s name actually represents the building, while 69% of images depicting the building are not annotated with the building&rsquo, s name. There was also an observation that for most landmarks, 97% of relevant building images were located within 300 m around the building in terms of spatial distribution.

International audience; In this work we introduce and evaluate a deep learning model, mbCNN, that combines together satellite imagery and Volunteer Geographical Information (VGI) data to deal with different types of built-up surfaces. Differently from most of the previous works that only consider Urban/Non-Urban settings involving only one urban LULC class, here, we investigate the possibility to go a step further and distinguish among several urban land use classes: Residential, industrial, sport fields and non-urban. Experiments on a real-world dataset covering the City of Montpellier (South of France) site are reported. Such results demonstrate the quality of Deep Learning approaches to deal with several types of Urban LULC mapping as well as the positive influence to integrate VGI knowledge in the process.

A realidade de um mundo global, reconfigura a relação entre acessibilidade (ou a sua ausência), exclusão social e desigualdade de género. Este relacionamento questiona o sistema de mobilidade e em particular as suas soluções universais e neutras desligadas das necessidades e aspirações de mobilidade e acessibilidade de mulheres e homens na sua relação com a produção e reprodução social. A estas questões associa­se a rápida evolução tecnológica e soluções que oferece aos quotidianos do espaço­tempo de mulheres e homens. Este artigo discute como podem os dispositivos móveis ( e ) ser usados para promover políticas de conciliação da mobilidade quotidiana, genericamente designada de ‘casa­trabalho’., La realidad de un mundo global, reconfigura la relación entre accesibilidad (o su ausencia), exclusión social y desigualdad de género. Este relacionamiento cuestiona el sistema de movilidad y en particular sus soluciones universales y neutras desligadas de la necesidad y aspiraciones de movilidad y accesibilidad de mujeres y hombres en relación con la producción y reproducción social. Estas cuestiones se asocian a la rápida evolución tecnología y soluciones que ofrecen a los cotidianos del espacio tiempo de mujeres y hombres. Este artículo discute como utilizan los dispositivos de movilidad cotidiana, genéricamente designada de ‘casa­trabajo’., The reality of a global world reconfigures the relationship between accessibility (or its absence), social exclusion and gender inequality. This relationship challenge the mobility system and in particular its universal and neutral solutions, disconnected from the needs and aspirations of mobility and accessibility of women and men in their relationship with social production and reproduction. These issues are associated with the fast­technological evolution and the solutions it offers to the daily space­time needs of women and men. This article discusses how mobile devices (smartphones and trackers) can be used to promote policies for reconciling daily mobility, usually called ‘home­work’ commuting.

Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.

After major geo-hydrological disasters, the rapid detection of both location and extension of the areas struck by floods and landslides is essential to manage recovery operations. In these cases, one of the most important tools required by decision-makers is a georeferenced map showing the affected areas. Preliminary maps reporting the potential location of landslides, for example, may also represents a starting point for field Geologists engaged in the production of detailed event landslides inventories, allowing optimize time and efforts. Optical and radar satellite images are experiencing an increasing use in the emergency operations due to improved revisiting times and spatial resolution. However, optical scenes are often useless for long times because of a permanent cloud cover or shadow effects hiding the collapsed mountain slopes. For this reason, the landslide research community is dedicating more efforts to implement new approaches based on satellite radar data, which typically are not affected by these types of disturbance. Even if the development of landslide inventories from radar images is still a challenge, such data represent a valuable support to generate archives of information acquired systematically, forming therefore clusters of "Big data" that can be useful in the context of susceptibility, hazard, and landslide risk analysis. In the innovative approach presented in this contribution, we faced many of the issues related to the use of satellite radar data for supporting the production of event landslides inventories. The research activities have been carried out in the framework of the STRESS (Strategies, Tools and new data for REsilient Smart Societies) project, devoted to design, implement and test a prototype of a Spatial Data Infrastructure (SDI) to support decision makers in the geo-hydrological risk management. Specifically, our group have implemented an automatic processing chain that, starting from the downloading of Sentinel-1 satellite images, produces a georeferenced map highlighting the likely location of landslides. The procedure exploits multi-temporal variations of the radar backscattered signal induced by significant land cover changes, which can be associated to the occurrence of rapid-moving landslides. This map is sent to local scientists and citizens by means of a Smart App developed to collect, store and analyze Volunteered Geographic Information (VGI), to ease and improve results obtained from satellites. The STRESS project is also aimed at promoting and exploiting VGI freely provided by citizens involved as "human sensors" in the production of landslide inventories, providing direct information if no evidences are available from remote sensing. Outcomes resulting from both remote sensing and VGI activities should thereby support the updating of existing landslides susceptibility models and hazard assessment. The test site of the project is located in the Central Alps (Italy), where the steep slopes are very prone to landslides and debris flow processes. However, a preliminary application of the automatic processing chain has been successfully performed for a wide area of the Papua New Guinea affected by dozens of landslides in 2018, as well as the implementation of the VGI Smart App has been completed and verified.

This paper aims to qualify the behaviour of contributors to OpenStreetMap (OSM), a volunteered geographic information (VGI) project, through a multigraph approach. The main purpose is to reproduce ...

Technological development during the first decade of this century provoked a boom of user-generated, web-shared media. In geography, such trend is called Volunteered Geographic Information; OpenStreetMap (OSM) is its most famed project. While several research has been made about OSM -particularly, concerning its quality-, none of such has been made in Ecuador in despite that, indeed, there has been mapping in this country. This paper proposes an Exploratory Data Analysis as first approach to understanding the features of OSM. Study area is the Manuela Sáenz administrative zone of Quito. Free GIS and programming software were used, while applying non-graphic and graphic methods. Results are displayed in several plots and phenomena are discussed, regarding number of keys and nodes, area condition, and building, highway and source keys. The paper concludes identifying OSM aspects for further research.

A informação geográfica é utilizada constantemente no processo de tomada de decisão relacionado a problemas geoespaciais. Com a disseminação da tecnologia de informação na sociedade, a geração de dados espaciais por meio do mapeamento baseado em Informação Geográfica Voluntária (VGI) supera os procedimentos convencionais de mapeamento em termos de custo, qualidade temática e simplicidade. Este artigo é o primeiro a considerar o uso de VGI e programas gratuitos para elaborar bases cartográficas com alta acurácia temática e temporal, sendo um bom primeiro passo para o desenvolvimento de pesquisas nesta área. Os resultados experimentais mostram o potencial da VGI como uma ferramenta para auxiliar gestores municipais no processo de decisão baseado em análises espaciais, permitindo uma melhor utilização dos recursos públicos, aumento da qualidade dos serviços oferecidos aos cidadãos e redução dos custos operacionais da administração pública.

Providing long-term data about the evolution of railway networks in Europe may help us understand how European Union (EU) member states behave in the long-term, and how they can comply with present EU recommendations. This paper proposes a methodology for collecting data about railway stations, at the maximal extent of the French railway network, a century ago.The expected outcome is a geocoded dataset of French railway stations (gares), which: (a) links gares to each other, (b) links gares with French communes, the basic administrative level for statistical information. Present stations are well documented in public data, but thousands of past stations are sparsely recorded, not geocoded, and often ignored, except in volunteer geographic information (VGI), either collaboratively through Wikipedia or individually. VGI is very valuable in keeping track of that heritage, and remote sensing, including aerial photography is often the last chance to obtain precise locations. The approach is a series of steps: (1) meta-analysis of the public datasets, (2) three-steps fusion: measure-decision-combination, between public datasets, (3) computer-assisted geocoding for ‘gares’ where fusion fails, (4) integration of additional gares gathered from VGI, (5) automated quality control, indicating where quality is questionable. These five families of methods, form a comprehensive computer-assisted reconstruction process (CARP), which constitutes the core of this paper. The outcome is a reliable dataset—in geojson format under open license—encompassing (by January 2021) more than 10,700 items linked to about 7500 of the 35,500 communes of France: that is 60% more than recorded before. This work demonstrates: (a) it is possible to reconstruct transport data from the past, at a national scale, (b) the value of remote sensing and of VGI is considerable in completing public sources from an historical perspective, (c) data quality can be monitored all along the process and (d) the geocoded outcome is ready for a large variety of further studies with statistical data (demography, density, space coverage, CO2 simulation, environmental policies, etc.).

As transport systems are pushed to the limits in many cities, governments have tried to resolve problems of traffic and congestion by increasing capacity. Miller (2013) contends the need to identify new capabilities (instead of capacity) of the transport infrastructure in order to increase efficiency without extending the physical infrastructure. Kenyon and Lyons (2003) identified integrated traveller information as a facilitator for better transport decisions. Today, with further developments in the use of geographic information systems (GIS) and a greater disposition by the public to provide volunteered geographic information (VGI), the potential of information is not only integrated across modes but also user-generated, real-time and available on smartphones anywhere. This geographic information plays today an important role in sectors such as politics, businesses and entertainment, and presumably this would extend to transport in revealing people’s preferences for mobility and therefore be useful for decision-making. The widespread availability of networks and smartphones offer new opportunities supported by apps and crowdsourcing through social media such as the successful traffic and navigation app Waze, car sharing programmes such as Zipcar, and ride sharing systems such as Uber. This study aims to develop insights into the potential of governments to use voluntary (crowdsourced) geographic information effectively to achieve sustainable mobility. A review of the literature and existing technology informs this article. Further research into this area is identified and presented at the end of the paper., peer-reviewed

In this paper, we critically explore the interplay of algorithms and civic participation in visions of a city governed by equation, sensor and tweet. We begin by discussing the rhetoric surrounding techno-enabled paths to participatory democracy. This leads to us interrogating how the city is impacted by a discourse that promises to harness social/human capital through data science. We move to a praxis level and examine the motivations of local planners to adopt and increasingly automate forms of VGI as a form of citizen engagement. We ground theory and praxis with a report on the uneven impacts of algorithmic civic participation underway in the Canadian city of Toronto.

This article describes a Los Angeles-based website that collects volunteered geographic information (VGI) on outdoor advertising using the Google Street View interface. The Billboard Map website was designed to help the city regulate signage. The Los Angeles landscape is thick with advertising, and the city efforts to count total of signs has been stymied by litigation and political pressure. Because outdoor advertising is designed to be seen, the community collectively knows how many and where signs exist. As such, outdoor advertising is a perfect subject for VGI. This paper analyzes the Los Angeles community's entries in the Billboard Map website both quantitatively and qualitatively. I find that members of the public are well able to map outdoor advertisements, successfully employing the Google Street View interface to pinpoint sign locations. However, the community proved unaware of the regulatory distinctions between types of signs, mapping many more signs than those the city technically designates as billboards. Though these findings might suggest spatial data quality issues in the use of VGI for municipal record-keeping, I argue that the Billboard Map teaches an important lesson about how the public's conceptualization of the urban landscape differs from that envisioned by city planners. In particular, I argue that community members see the landscape of advertising holistically, while city agents treat the landscape as a collection of individual categories. This is important because, while Los Angeles recently banned new off-site signs, it continues to approve similar signs under new planning categories, with more in the works.

High density residential development in metropolitan Melbourne, where contradictory imperatives of neighbourhood character and urban intensification play important roles, remains an uncertain practice. One key issue for plan implementation is the lack of consistency between authorities, developers and the community in interpreting the standards, design guidelines, and state/local strategies, especially those relating to neighbourhood character. There is currently no mechanism to incorporate community perceptions and place experiences as subjective aspects of neighbourhood character in development assessments. There is also little use of micro-scale and multi-dimensional spatial analysis to integrate these subjective aspects with objective measures (e.g. building volume and height; streetscape) to communicate effectively—and in a limited timeframe—with all stakeholders. This paper explores the potential of two emerging geospatial technologies that can be leveraged to respond to these problems. Evidence in the literature suggests that volunteered geographic information (VGI) can provide community input around subjective aspects of the urban environment. In addition, a deluge of three-dimensional (3D) spatial information (e.g. 3D city models) is increasingly available for micro-level (building- or property-level) assessment of the physical aspects of the urban environment. This paper formulates and discusses a conceptual framework to link these two spatial technological advancements in a virtual geographic environment (VGE) that accounts for micro-scale 3D spatial analysis incorporating both subjective and objective aspects of neighbourhood character relevant in implementing compact city strategies.

There is much interest in being able to combine crowdsourced data. One of the critical issues in information sciences is how to combine data or information that are discordant or inconsistent in some way. Many previous approaches have taken a majority rules approach under the assumption that most people are correct most of the time. This paper analyses crowdsourced land cover data generated by the Geo-Wiki initiative in order to infer the land cover present at locations on a 50 km grid. It compares four evidence combination approaches (Dempster Shafer, Bayes, Fuzzy Sets and Possibility) applied under a geographically weighted kernel with the geographically weighted average approach applied in many current Geo-Wiki analyses. A geographically weighted approach uses a moving kernel under which local analyses are undertaken. The contribution (or salience) of each data point to the analysis is weighted by its distance to the kernel centre, reflecting Tobler’s 1st law of geography. A series of analyses were undertaken using different kernel sizes (or bandwidths). Each of the geographically weighted evidence combination methods generated spatially distributed measures of belief in hypotheses associated with the presence of individual land cover classes at each location on the grid. These were compared with GlobCover, a global land cover product. The results from the geographically weighted average approach in general had higher correspondence with the reference data and this increased with bandwidth. However, for some classes other evidence combination approaches had higher correspondences possibly because of greater ambiguity over class conceptualisations and / or lower densities of crowdsourced data. The outputs also allowed the beliefs in each class to be mapped. The differences in the soft and the crisp maps are clearly associated with the logics of each evidence combination approach and of course the different questions that they ask of the data. The results show that discordant data can be combined (rather than being removed from analysis) and that data integrated in this way can be parameterised by different measures of belief uncertainty. The discussion highlights a number of critical areas for future research.

This paper investigates research using VGI and geo-social media in the disaster management context. Relying on the method of systematic mapping, it develops a classification schema that captures three levels of main category, focus, and intended use, and analyzes the relationships with the employed data sources and analysis methods. It focuses the scope to the pioneering field of disaster management, but the described approach and the developed classification schema are easily adaptable to different application domains or future developments. The results show that a hypothesized consolidation of research, characterized through the building of canonical bodies of knowledge and advanced application cases with refined methodology, has not yet happened. The majority of the studies investigate the challenges and potential solutions of data handling, with fewer studies focusing on socio-technological issues or advanced applications. This trend is currently showing no sign of change, highlighting that VGI research is still very much technology-driven as opposed to theory- or application-driven. From the results of the systematic mapping study, the authors formulate and discuss several research objectives for future work, which could lead to a stronger, more theory-driven treatment of the topic VGI in GIScience. Carlos Granell has been partly funded by the Ramón y Cajal Programme (grant number RYC-2014-16913)

Quality of data is one of the key issues in the domain of Volunteered geographic information (VGI). To this purpose, in literature VGI data has been sometime compared with authoritative geospatial data. Evaluation of single contributions to VGI databases is more relevant for some applications and typically relies on evaluating reputation of contributors and using it as proxy measures for data quality. In this paper, we present a novel approach for reputation evaluation that is based on the well known PageRank algorithm for Web pages. We use a simple model for describing different versions of a geospatial entity in terms of corrections and completions. Authors, VGI contributions and their mutual relationships are modeled as nodes of a graph. In order to evaluate reputation of authors and contributions in the graph we propose an algorithm that is based on the personalized version of PageRank.

OpenStreetMap is a geographic information platform designed to provide real-time updates and user-generated content related to its freely available global map, and it is one of the most widely used examples of volunteered geographic information, a technique associated with so-called neogeography. This paper, based on the data from China’s OpenStreetMap road network in May 2014, taking 340 prefecture-level cities in China as its study area, presents the geometric-related (road density) and attribute-related (type diversity) spatial patterns of the OpenStreetMap road network, and explores their relationship. The results are as follows. (1) The distribution of OpenStreetMap road density in Shenzhen, Shanghai, Hong Kong, and Macao predominantly obeys a “positive skewness distribution”. OpenStreetMap data for eastern China shows a higher overall and circular structure. In central China, there are noticeable discrepancies in the road density, whereas in western China, the road density is low. (2) The OpenStreetMap road diversity shows a normal distribution. The spatial pattern for the so-called “Hu Huanyong line” was broken by the effect of diplomatic and strategic factors, showing a high diversity along the peripheral border, coastal cities, and core inland cites. (3) China’s OpenStreetMap is partitioned into four parts according to road density and diversity: high density and high diversity; low density and low diversity; high density and low diversity; and low density high diversity. (4) The OpenStreetMap geographical information-collection process and mechanism were analyzed, demonstrating that the road density reflects the preponderance of traffic in the real world. OpenStreetMap road diversity reflects the road-related geographic information demand and value, and it also reflects the interests of users toward to OpenStreetMap geographical information.

The need for in-depth and fast observation of waste pollutions, increase in public concerns on environmental pollution and the effect of polluted environment on the physical and mental health of people have led to participatory environmental monitoring and protection. Citizens can act as volunteer, interactive, group, logical, smart, responsible, mobile, and dispersed observers/sensors to constantly monitor their surroundings and provide real-time data of waste pollutions that are not easily remotely sensed. The concept of volunteered geographic information (VGI) can be adopted as an effective phenomenon for participatory collection of environmental pollution data. However, the research problem is how to effectively use these citizen-contributed or volunteered information for waste management analyses and decision-making processes. Consequently, the objective of the present study is to develop a VGI-based environmental decision support tool that facilitates direct involvement of the public in generating waste pollution data and provides appropriate GIS-MCDA (multicriteria decision analysis) analytical tools for waste management and decision making using the citizen-contributed data. A web-based prototype of the decision support system was developed to demonstrate the practical feasibility, applicability and some functionalities of the system for pollution-related decision analyses.

A comprehensive understanding of urban areas includes deep knowledge about locations and functions of buildings within. In most developed countries cadastral data is available for this research, but from a global perspective the only free and comprehensive data source is OpenStreetMap (OSM). For this study we selected 42 cities across the globe covering a wide range of climate zones as well as cultures and assess the accuracy of building function labels in OSM indirectly by comparing them to Google Places. We state that points-of-interest (POIs) are reasonably covered in Google Places due to a large number of users and a business perspective driving its development. We study how many semantic building tags are in accordance with the proposed scheme by OSM. In this regard Los Angeles has the best coverage, followed by Amsterdam and Cologne. In Melbourne, Paris, and Sydney we find the most matching building functions of OSM and Google Places. In summary, we conclude that OSM is not ready to provide ground truth labels for each place on the globe, but can serve as a powerful and rich label source in selected study areas. Our study gives a first insight where obtaining training labels from OSM is a valid and reliable approach.

The adoption of location-based information sharing technologies, and the emergence of volunteered geographic information (VGI), has seen changes to community involvement in disaster management. The concept of resilience, and recognition of the capacity for renewal, re-organization, and societal development, has gained currency in disaster management. However, the opportunities presented by spatially referenced data for sourcing contextual information for understanding processes of social–ecological resilience and fostering local inclusion has not been examined. We examine how web 2.0 platforms, including VGI and social media, can support resilience building, and critically evaluate how these technologies potentially undermine resilience. We concentrate our analysis on factors deemed important for community disaster resilience through review of recent literature, policy documents, and author experience. Establishing which elements of VGI in disaster management should be emphasized, such as increased flexibility or individual empowerment, and which require careful management, such as compromised privacy or data quality, will enable VGI to become less opportunistic, data-centric, disruptive, and exclusionary, and allow for more reliable, community-centric, complementary, and socially inclusive practices. Incorporating awareness and training on collaborative geoweb technologies into disaster preparedness programs will equip individuals to make informed judgments on VGI content and reduce unintended consequences of social media initiatives.

The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007&ndash, 2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions.

International audience; With the advent of Volunteered Geographic Information, more and more citizens collect geospatial data in several application domains (urban, agriculture, etc.). Usually, volunteers are data producers and passive consumer of VGI data analysis provided by organisms/enterprises that manage collected data. This “bottom-up data supply and top-down data analysis” paradigm represents an important barrier for the development of volunteer observatories, since data producers feel excluded from the decision-making process. Therefore, in this position paper we propose a new Spatial OLAP (SOLAP) system where volunteers are active participants to the definition of SOLAP applications. We detail open issues and related works of this new GeoBusiness Intelligence systems.

Environmental noise is a big problem related to the environmental pollution in cities, which affects the quality of people life. In this paper, a methodology that uses an approach based on Volunteered Geographic Information (VGI) for the monitoring, analysis and prediction of environmental noise is proposed. It can be very useful to propose alternatives and initiatives that improve the life in a city. So, this work is composed of the following stages: data acquisition, analysis and, data processing, as well as the information visualization, considering the temporality of the same and taking into account macro and micro levels of analysis for the study surface. In addition, some details of the design and development of a geographic information system are presented, consisting of a web-mapping system, an application for mobile devices called “NoiseMonitor”, geospatial analysis and machine learning methods (support vector machines and artificial neural networks) for the prediction of environmental noise; by using contextual information; that is, some data related to the city. This kind of work seeks to take advantage of the willingness of citizens to participate collaboratively to sense their environment and be considered as human sensors, which unlike traditional approaches, the cost associated with the development and implementation of this project is much lower. Likewise, a case study based on the Mexico City is presented and discussed, particularly the fourth quadrant of the Historic Center of the City, which is very representative for the variety of environmental noise that is generated in that area. The application domain of this approach is oriented towards big data from a collaborative perspective, Internet of Things and smart cities.

In times of crisis, it is a priority to get help as soon as possible and any kind of help can be accepted. Especially, all kinds of support by volunteers, is a great help in crisis resolution. Crisis locations can be developed or less developed regions. It is sometimes difficult to reach help and support in less developed regions. To create Geographic data, which is an important requirement in crisis situations, is time-consuming and costly when attempted to be produced by conventional methods. When the data is produced by volunteers, it is faster and less costly because experts try to reach and support the region. Volunteered Geographic Information (VGI) is spatial data that has been contributed for free by volunteers. In our work, that is review, the examples of Volunteered Geographic Information applications in crisis management will be shown on the poster.

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset (“ground truth dataset”). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions.

In the last number of years there has been increased interest from researchers in investigating and understanding the characteristics and backgrounds of citizens who contribute to Volunteered Geographic Information (VGI) and Citizen Science (CS) projects. Much of the reluctance from stakeholders such as National Mapping Agencies, Environmental Ministries, etc. to use data and information generated and collected by VGI and CS projects grows from the lack of knowledge and understanding about who these contributors are. As they are drawn from the crowd there is a sense of the unknown about these citizens. Subsequently there are justifiable concerns about these citizens’ ability to collect, generate and manage high quality and accurate spatial, scientific and environmental data and information. This paper provides a meta review of some of the key literature in the domain of VGI and CS to assess if these concerns are well founded and what efforts are ongoing to improve our understanding of the crowd.

Die digitalen Stadtpl��ne vieler kleiner bis mittelgro��er St��dte in Europa sind bei n��herer Betrachtung ungenau bzw. unvollst��ndig. Ursachen daf��r sind neben unzureichender, rudiment��rer Kartierung die schnelle ��nderung der Bestandssituation wie sie beispielsweise bei Wiederaufbauarbeiten nach Naturkatastrophen zur Tage tritt. Ein weiteres Problem stellt das Versagen von automatisierten Verfahren bei ungew��hnlichen Situationen dar, wie tempor��re Einrichtungen (beispielsweise Bauger��ste) im zu kartierenden Bereich. Die Folge ist, dass grundlegende Informationen wie Stra��enverl��ufe, Grundrisse von Geb��uden, Adressen, Points of Interest falsch dargestellt werden oder schlichtweg nicht vorhanden sind. Dieser Umstand ist f��r die Anwohner wie Besucher einer Stadt ein nicht unerheblicher Nachteil und muss behoben werden. Anstatt einer klassischen Vermessung wurde der Versuch unternommen die Altstadt einer mittelgro��en Stadt durch eine Kombination aus VGI, Luftbildaufnahmen und bestehenden, alten Pl��nen zu kartieren. Dadurch sollten hohe Vermessungskosten und der damit verbundene Zeitaufwand reduziert werden. Der hohen ��nderungsrate und der Pr��senz au��ergew��hnlicher Situation wird durch VGI ���direkt��� begegnet und eine m��glichst aktuelle, zeitnahe Kartierung erm��glicht. Als Testgebiet f��r die praktische Durchf��hrung wurde die Altstadt von von LAquila / Italien gew��hlt. Auf Basis der in der Praxis gewonnen Erkenntnisse wurde ein Regelwerk nach dem Vorbild historischer Messinstruktionen f��r die VGI-Kartierung kleiner bis mittelgro��er St��dte auf Basis von OpenStreetMap aufgestellt. Dadurch erhalten diese Kommunen und interessierte OpenStreetMap-Kartographen eine Anleitung und somit die M��glichkeit zur selbst��ndigen, effizienten, qualitativ hochwertigen und kosteng��nstigen Kartierung., The digital city maps of many small to medium-sized cities in Europe are on closer inspection inaccurate or incomplete. This is due not only to inadequate, rudimentary mapping, but also to the rapid change in the stock situation, as is the case, for example, with reconstruction work following natural catastrophes. Another problem is the failure of automated procedures in unusual situations, such as temporary facilities (for example scaffolding) in the area to be mapped. The result is that basic information such as street profiles, floor plans of buildings, addresses, points of interest are misrepresented or simply do not exist. This circumstance is a not inconsiderable disadvantage for the residents and visitors of a city and must be remedied. Instead of a classic survey, the attempt was made to map the old town of a medium-sized city by a combination of VGI, aerial photographs and existing, old plans. This reduces high survey costs and the associated time expenditure. The high rate of change and the presence of extraordinary situations was addressed by VGI "directly" and a timely mapping as up-to-date as possible was possible. The test area for the practical implementation was the old town of L'Aquila / Italy. On the basis of the findings gained in practice, a set of rules based on historical measurement instructions for the VGI mapping of small to medium-sized cities based on OpenStreetMap was set up. This gives the communities and interested OpenStreetMap cartographers guidance and thus the opportunity for independent, efficient, high-quality and cost-effective mapping.

Long-term ecological research (LTER) is performed in many countries across the globe by scientists that gather and analyse multidecadal ecological observations and data, to support understanding and management of the environment. LTER data are used to describe the state and dynamics of an ecosystem. This valuable research is in charge of experts in ecology, and it is performed in the so-called LTER networks (for Europe, see http://www.lter-europe.net), usually organized at a national level; they consist of sites covering different ecosystem typologies, each one with research and monitoring facilities. In summer 2015, researchers of the Italian LTER network (LTER-Italy) have created a set of naturalistic trails, connecting sites of LTER-Italy (http://www.lteritalia.it/cammini), aiming to transfer and share the research results with citizens, through public events and informal science communication. Along one of these trails, performed on the Alps, the organizers proposed the use of two VGI apps to collect either biological or abiotic observations (http://www.lteritalia.it/it/content/citizenscience). The apps were chosen according to crucial characteristics, i.e. offline usage, operative system independence, strong development and support community, customization of user interfaces. This paper reports on this first test, discussing lessons learned and, in particular, the impact on the practice of research communities of participative, innovative tools, often neglected in the analysis of mobile application effects.

Les portails scientifiques ont un intérêt grandissant quand nous voyons le nombre d'articles présents et ne cessant d'augmenter. Pouvoir trouver l'article qui correspond à notre besoin à un moment précis, c'est un problème récurrent auquel nous nous confrontons régulièrement. Il faut parfois passer plusieurs heures pour cibler réellement son problème, comprendre le vocabulaire qui le caractérise bien que l'idée peut paraˆıtre claire au début. Nous voudrions améliorer le concept de portail scientifique pour le rendre plus agréable et accessible d'utilisation. L'avantage que nous pouvons avoir dans ce cas de figure par rapport à un moteur de recherche est que nous nous basons sur une plus faible quantité de données. Notre but n'est pas de répertorier l'ensemble des articles du web mais de cibler un domaine précis comme nous le ferons avec les données de type ”VGI1”. Nous voulons donner accès à une nouvelle possibilité de visualisation de ces portails de manière plus intuitive.

International audience; Vandalism is a phenomenon that has affected by now the digital domain, in particular in the context of Volunteered Geographic Information projects. This paper aims at proposing a methodology to detect vandalism in the OpenStreetMap project. First, an analysis of related works sheds light on the lack of consensus when it comes to defining vandalism in VGI from both conceptual and practical points of view. Second, we present experiments on the use of clustering-based outlier detection methods to identify vandalism in OSM. The outcome of this study focuses on choosing the right variables when it comes to detecting vandalism in OSM.

Effective transport infrastructure is an essential component of economic integration, accessibility to vital social services and a means of mitigation in times of emergency. Rural areas in Africa are largely characterized by poor transport infrastructure. This poor state of rural road networks contributes to the vulnerability of communities in developing countries by hampering access to vital social services and opportunities. In addition, maps of road networks are incomplete, and not up-to-date. Lack of accurate maps of village-level road networks hinders determination of access to social services and timely response to emergencies in remote locations. In some countries in sub-Saharan Africa, communities in rural areas and some in urban areas have devised an alternative mode of public transport system that is reliant on motorcycle taxis. This new mode of transport has improved local mobility and has created a vibrant economy that depends on the motorcycle taxi business. The taxi system also offers an opportunity for understanding local-level mobility and the characterization of the underlying transport infrastructure. By capturing the spatial and temporal characteristics of the taxis, we could design detailed maps of rural infrastructure and reveal the human mobility patterns that are associated with the motorcycle taxi system. In this study, we tracked motorcycle taxis in a rural area in Kenya by tagging volunteer riders with Global Positioning System (GPS) data loggers. A semi-automatic method was applied on the resulting trajectories to map rural-level road networks. The results showed that GPS trajectories from motorcycle taxis could potentially improve the maps of rural roads and augment other mapping initiatives like OpenStreetMap (VLID)2861700

This chapter discusses a multidisciplinary research project called Urban Nexus, which aims at studying the role of big data in urban governance, along with the need of a high-level query language to allow analysts, geographers and, in general, non-programmers to easily cross-analyze multisource big data produced by the inhabitants and coming also from other sources of information. The project focuses on the production of Volunteered Geographic Information by means of apps, crowdsourced data from social networks and fosters an analysis based also on authoritative geo-referenced data coming from multiple institutional sources. The Urban Nexus project applies novel and multidisciplinary approaches to interpret social aspects of big data, in relation to three medium-sized cities and their surrounding areas: Bergamo in the wider context of Milan (Italy), Lausanne and the other cities of Switzerland and Cambridge and the metropolitan area of London (UK). Focused on urban regeneration and mobility, we want to devise and test methods to make such cities learning cities, i.e., territories where the inhabitants help to identify the design guidelines according to their competence on places that is their spatial capital [1]. In particular, we identified an analysis method of data sets represented by means of the JSON lightweight data-interchange format. Currently, JSON has become the de facto standard for exchanging Volunteered Geographic Information (VGI) and crowdsourced data created within social networks. Since an easy to use high-level language for querying and manipulating collections of possibly geo-tagged JSON objects is still unavailable, as well as an integrated framework for data management and visualization of geo-tagged JSON data sets, we devised a new framework named J-CO. In this framework, we propose a high-level language, called J-CO-QL (firstly introduced in [2]), which is based on a well-defined execution model. A plug-in for QGIS is provided as well: it permits to visualize geo-tagged data sets stored in a NoSQL database such as MongoDB; furthermore, the same plug-in can be used to write and execute J-CO-QL queries on those databases. By means of a practical example, we show how the J-CO framework is able to allow geographers and analysts to work on complex data sets coming from VGI sources, in order to study the mobility of city users who voluntarily participated to the gathering process of data for the city of Bergamo.

Da molti anni la Commissione - nell'ambito delle azioni dedicate a rafforzare il legame tra scienza e società - invita i ricercatori a promuovere la cooperazione con soggetti esterni, dagli stakeholder ai cittadini, a comunicare di più e meglio, a valorizzare i risultati della propria ricerca dimostrando che essa può avere un impatto (sociale ed economico) sulla società. Ma ancora oggi queste attività sono considerate come non strettamente attinenti allo sviluppo della ricerca, se non proprio estranee, dalla maggior parte dei ricercatori, che spesso le delegano a figure esterne, ritenendosi poco adeguati (o non tenuti) a svolgerle. Con il risultato di acuire la separazione invece di rafforzare il legame tra scienza e società. 1 Questo atteggiamento, con il nuovo approccio della RRI, richiesto in tutti i progetti finanziati nell'ambito del programma Horizon2020 (H2020), non è più possibile, almeno in via teorica. Nella RRI si chiede infatti ai ricercatori di attuare un cambio del proprio modo di operare, "anticipando" i possibili impatti delle attività, "riflettendo" sulle azioni intraprese o da programmare durante le fasi, da quelle svolte quotidianamente fino alle interazioni personali con tutti gli attori dell'innovazione. Nella nuova configurazione del rapporto tra scienza e società auspicato da H2020 ci si aspetta dunque qualcosa di diverso e di più di una semplice adesione formale a delle norme o strumentale per accedere a un finanziamento comunitario. Per parafrasare il titolo di un report europeo (Felt 2007), la RRI chiede a tutti gli attori di "prendere sul serio" la responsabilità nella società della conoscenza, a cominciare dalla riflessione su come sta cambiando il proprio specifico ambito di ricerca e sulle implicazioni che il cambiamento e l'innovazione proposta possono avere per le responsabilità di tutti gli attori. E' quanto abbiamo provato a fare con il progetto "Strategies and Tools for REsilient Smart Societies" (dall'acronimo STRESS) che descriviamo brevemente in questo capitolo.

The chapter investigates the kinds of imperfection affecting Volunteer Geographic Information (VGI) created by users eager to participate in some citizen science project. An approach based on the use of fuzzy domain ontologies and level-based approximate reasoning is suggested to represent and manage both the uncertainty of volunteers when describing their observations and the vagueness of ill-defined domain knowledge. This way one can model more reliable smart applications for creating VGI as well as can design less ambiguous spatial data infrastructures (SDIs) for sharing VGI with final stakeholders.

Oggi si parla sempre piu’ spesso di “Citta intelligente” o “Smart City, un concetto che non può prescindere da una stretta collaborazione tra cittadini e Pubblica Amministrazione nella gestione dei processi decisionali in ambito urbano finalizzati alla fornitura di servizi sempre più efficienti, ma soprattutto ad un miglioramento delle condizioni della vita e della sicurezza sia del singolo individuo che dell’intera collettività. Il processo partecipativo del cittadino alla gestione del bene comune e in particolare della sicurezza in ambito urbano, è un progetto molto ambizioso che passa attraverso l’instaurazione di un clima di fiducia e di collaborazione tra collettività e Pubblica Amministrazione.

The integration of user-generated content made in a collaborative environment is being increasingly considered a valuable input to reference maps, even from official map agencies such as USGS and Ordnance Survey. In Brazil, decades of lack of investment has resulted in a topographic map coverage that is both outdated and unequally distributed throughout the territory. This paper aims to analyze the spatial distribution of updates of OpenStreetMap in rural and urban areas in the country to understand the patterns of user updates and its correlation with other economic and developmental variables. This analysis will contribute to generating the knowledge needed in order to consider the use of this data as part of a reference layer of the National Spatial Database Infrastructure as well to design strategies to encourage user action in specific areas.