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The quality of Volunteered Geographic Information (VGI) is currently a topic that question spatial data users as well as authoritative data producers who are willing to exploit the benefits of crowdsourcing. Contrary to most authoritative databases, the advantage of VGI provides open access to spatial data. However, VGI is prone to errors, even to deliberate defacement perpetrated by ill-intended contributors. In the latter case, we may speak of cartographic vandalism of carto-vandalism. This phenomenon is one the main downsides of crowsdsourcing, and despite the small amount of incidents, it may be a barrier to the use of collaborative spatial data. This thesis follows an approach based on VGI quality -- in particular, the objective of this work is to detect vandalism in spatial collaborative data. First, we formalize a definition of the concept of carto-vandalism. Then, assuming that corrupted spatial data come from malicious contributors, we demonstate that qualifying contributors enables to assess the corresponding contributed data. Finally, the experiments explore the ability of learning methods to detect carto-vandalism; La qualité de l'information géographique volontaire est actuellement un sujet qui questionne autant les consommateurs de données géographiques que les producteurs de données d'autorité voulant exploiter les bienfaits de la démarche collaborative. En effet, si les données cartographiques collaboratives présentent l'intérêt d'être ouvertes, contrairement à certaines bases de données géographiques officielles, celles-ci sont néanmoins sujettes à des erreurs voire à des dégradations volontaires, provoquées par des contributeurs malintentionnés. Dans ce dernier cas, on parle de vandalisme cartographique ou de carto-vandalisme. Ce phénomène est un travers de la démarche collaborative, et bien qu'il ne concerne qu'une faible portion des contributions, il peut constituer un obstacle à l'utilisation des données cartographiques participatives. Dans une démarche de qualification de l'information géographique volontaire, ce travail de thèse a plus précisément pour objectif de détecter le vandalisme dans les données collaboratives cartographiques. Dans un premier temps, il s'agit de formaliser une définition du concept de carto-vandalisme. Puis, en partant du principe que les contributions volontairement dégradées proviennent de contributeurs malveillants, nous cherchons à démontrer que la qualification des contributeurs permettant d'évaluer leurs contributions. Enfin, nos expériences explorent la capacité des méthodes d'apprentissage machine (machine learning) à détecter le carto-vandalisme

Data warehouses (DWs) are widely known for their powerful analysis capabilities that serve either for historic data investigation or for predictions of potentially continuous phenomena. However, they are still in most cases limitedly used except by enterprises or governments while, with the huge amounts of data produced and collected by the Web2.0 technologies, many other unusual users might benefit from analysing their data if DWs are properly dedicated to their specific needs. They might be association adherents, online community members, observatory volunteers, etc. Unlike in classical contexts, requirements engineering RE with volunteers lacks group cohesion and straightforward strategic objectives. This is hence because they come with different backgrounds and they do not have an acknowledged representative leadership, which would very likely lead to multiple contradictory interpretations of the data and consequently of conflictual requirements. When stakeholders have divergent goals, it becomes problematic to maintain an agreement between them, especially when it comes to eliciting DW requirements whose future use is meant to serve as larger interested public as it possibly could. In this work, we propose a new generic and participative DW design methodology that relies on a Group Decision Support System (GDSS) to support the collaboration of the engaged volunteers. We suggest in this methodology two RE scenarios, (i) using GDSS for a collaborative elicitation when groups of users with common objectives are identifiable or (ii) with pivot tables and rapid prototyping formalisms when only individual volunteers are participating. Then, we reduce the number of the resulting models by fusing them based on their multidimensional (MD) similarities. The fused models require a further refinement that focuses on solving the remaining subject matter inconsistencies that are due to either erroneous definitions of unspecialized volunteers or to conceptually admissible, but irrelevant to the application domain, newly generated elements after the fusion. This is handled by the “collaborative resolution of requirement conflicts” step that we defined two methods for its execution. The first is a simplified collaborative method that we evaluate in which each model’s MD elements against a reduced number of criteria that apply for each component’stype using an existing GDSS that allows the collaborative process execution. The second is a profile-aware method that we suggest for which a more detailed set of evaluation criteria and adaptability of the collaborative process to allow its use by both crowdsourcing and enterprise DW design projects. As GDSS are designed to support a group engaged in a collective decision process, which is the main tool that we rely on which in two stages of our methodology i.e. RE and collaborative refinement of the fused models, we also propose a new GDSS that we adopted in its architecture the concept of Thinklets i.e. a well-known design pattern for collaborative processes. In addition to the group activities reproducibility that offers the concept of Thinklets, we have as well implemented a recommender system prototype that is mainly based on a hierarchical division of decision categories and an automatization of certain assistive functionalities to allow a guided and appropriate use of the system devoted to the facilitator. This has been done after a set of experiments conducted with real volunteer users engaged in solving risk management and uncertainty group problems. The new GDSS that we suggest introduces a customized implementation of certain Thinklets in order to improve their suitability to our methodology as well as for novice and inexperienced users from a more general perspective. In addition to that, we propose a new Thinklet, namely CollaborativeDW, that allows a fluid configuration and dynamic execution of our second refinement method i.e. the profile-aware approach, and that we have tested with real users., Les entrepôts de données (EDs) sont connus pour leurs puissantes capacités d'analyse qui servent soit à la fouille de données historiques soit à la prévision de phénomènes potentiellement continus. Cependant, dans la plupart des cas, ils sont encore utilisés de manière limitée, sauf par les entreprises ou les gouvernements alors que, avec les énormes quantités de données produites et collectées par les technologies Web 2.0, de nombreux autres utilisateurs inhabituels pourraient bénéficier de l'analyse de leurs données si les EDs sont correctement dédiés à leurs besoins spécifiques. Il peut s'agir d'adhérents à une association, de membres d’une communauté en ligne, de volontaire d’un observatoire, etc. Contrairement aux contextes classiques, l’ingénierie des exigences (IE) avec les volontaires manque de cohésion de groupe et d'objectifs stratégiques précis. En effet, ils viennent d'horizons différents et n'ont pas un leadership représentatif reconnu, ce qui conduirait très probablement à de multiples interprétations contradictoires des données et par conséquent à des exigences conflictuelles. Lorsque les parties prenantes ont des objectifs divergents, il devient problématique de maintenir un accord entre elles, en particulier lorsqu'il s'agit d’éliciter des exigences d'ED dont l'utilisation future est destinée à servir le plus grand public intéressé possible. Dans ce travail, nous proposons une nouvelle méthodologie de conception participative d’ED, qui s'appuie sur un système d'aide à la décision de groupe (GDSS). Nous proposons deux scénarios d'IE (i) utiliser le GDSS pour une élicitation collaborative lorsque des groupes d'utilisateurs ayant des objectifs communs sont identifiables ou (ii) avec des tableaux croisés dynamiques et des formalismes de prototypage rapide lorsque seuls des volontaires individuels y participent. Ensuite, nous réduisons le nombre de modèles résultants en les fusionnant en fonction de leurs similitudes multidimensionnelles (MD). Les modèles fusionnés nécessitent une amélioration supplémentaire qui se concentre sur la résolution des incohérences causées soit par des définitions erronées de volontaires non spécialisés, soit par des éléments, conceptuellement admissibles mais sans rapport avec le domaine d'application, qui sont nouvellement générés après la fusion. Ceci est géré par la résolution collaborative des conflits d'exigences que nous avons défini au travers des deux méthodes précédemment évoquées. (i) Une méthode collaborative simplifiée que nous évaluons dans laquelle les éléments MD par rapport à un nombre réduit de critères en utilisant un GDSS existant qui permet l'exécution du processus collaboratif (PC). (ii) Une méthode sensible au profil que nous suggérons pour laquelle un ensemble plus détaillé de critères d'évaluation et une adaptabilité du PC pour permettre son utilisation à la fois dans des projets de crowdsourcing et d'entreprise. Nous proposons également un nouveau GDSS dans lequel nous nous sommes inspirés dans son architecture du concept de Thinklet, qui est un modèle de conception bien connu dans la littérature pour les PCs. En plus de la reproductibilité des activités de groupe qu’offrent les Thinklets, nous avons implémenté un prototype d’un système de recommandation qui est basé sur une hiérarchisation des catégories de décision pour permettre une utilisation guidée et appropriée du système pour le facilitateur. Cela a été testé par des expériences menées avec de vrais utilisateurs volontaires engagés dans la résolution de problèmes de gestion de risques et d'incertitude. Nous introduisons dans ce GDSS une implémentation personnalisée de certains Thinklets afin d'améliorer leur adéquation à notre méthodologie ainsi qu'aux utilisateurs novices et inexpérimentés dans une perspective plus générale. En plus de cela, nous proposons un nouveau Thinklet, à savoir CollaborativeDW, qui permet une configuration dynamique de notre deuxième méthode de résolution de conflits, et que nous avons testée avec de vrais utilisateurs.

International audience; Les données géographiques présentes dans OpenStreetMap (OSM), décrites à la fois par leur géométrie et de la sémantique, constituent une source d'informa-tion essentielle à la fabrication de cartes en relief pour déficients visuels. Cependant, à l'échelle du carrefour, les détails géométriques nécessaires à la modélisation de certains éléments ne sont pas disponibles. L'utilisation de l'imagerie aérienne constitue une source d'information complémentaire, mais impose un traitement d'image sophistiqué. Dans cet article, nous proposons une approche basée sur l'apprentissage profond (ré-seau adverse génératif conditionnel), en enrichissant les informations présentes dans les orthophotographies par des données sémantiques et géométriques issues d'OSM. Afin de mesurer l'influence de cet enrichissement, nous présentons les résultats de deux séries d'apprentissage, avec et sans enrichissement. ABSTRACT. The geographic data in OpenStreetMap (OSM), described through geometry and semantics, is an essential source of information for relief maps production for the visually impaired. However, at the crossroads scale, the geometric details required for modeling some components are not available. The use of aerial imagery is a complementary source of information, but involves advanced image processing. In this article, we propose an approach based on deep learning (conditional Generative Ad-versarial Networks), by enhancing the information of orthophotographs with semantic and geometric data from OSM. In order to measure the influence of this enrichment, we present the results of two series of learning, with and without enrichment. MOTS-CLÉS : apprentissage machine ; enrichissement de données ; information géo-graphique volontaire ; segmentation sémantique

International audience

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.

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 ...

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.

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.

Despite the considerable growth in Volunteered Geographic Information (VGI) activities in citizen sensing and the evident opportunities for VGI use in map revision and updating, few European National Mapping Agencies (NMAs) or other types of government bodies have engaged significantly with VGI. Moreover, the level of engagement of NMAs with the VGI community varies greatly, and most of them have proposed their own tools for encouraging citizens and public partners to collect feedback or new data. There are numerous barriers limiting the participation of citizens and public partners in NMA data collection, including data quality issues, the motivation of the contributors and legal issues. The aim of this chapter is to give an overview of the experiences of some European NMAs in engaging with VGI. Guidelines and recommendations to support wider engagement with the VGI community are also proposed to help NMAs and interested government bodies exploit the potential of VGI for authoritative mapping. COST Action TD 1202 (Mapping and the Citizen Sensor)

International audience; In this conference, we present the NoiseCapture application and the OnoM@p Spatial Data Infrastructure, dedicated to noise purposes.This presentation take place within the European ENERGIC-OD project final conference (https://www.energic-od.eu/).

Noise stands for an important human health and environmental issue. Indeed, noise causes annoyance and fatigue, interferes with communication and sleep, damages hearing and entails cardiovascular problems (WHO, 2011) [1]. From an environmental point of view, noise implies a lessening of both the richness and abundance of the animal species, an alteration of the communication, which can threaten the reproduction and predation, etc. (Newport et al., 2014; Shannon et al., 2014) [2], [3]. Consequently, effects related to environmental noise result in a huge cost for society, with 2.2 billion euros in France, for example, for the year 2013 (Bourges and Diel, 2015) [4]. In this context, the reduction of noise in the environment is a burning issue, which requires, firstly, carrying out an evaluation of noise in the environment, and secondly, to establish action plans to reduce noise annoyance. With the development of the concept of participatory measurement, and considering the extremely large number of people equipped with a smartphone while being "in mobility", the use of smartphones is potentially a relevant solution to realize a large-scale environmental noise evaluation. The data presented hereinafter are collected from the Android NoiseCapture application and shared from the OnoMap Spatial Data Infrastructure (SDI). The NoiseCapture approach consists in measuring noise along a path, and then to share data with the community. This approach has been developed within the framework of the European ENERGIC-OD1 project, which aims at deploying a set of Virtual Hubs (VH) to share heterogeneous data with third parties, in respect with the European INSPIRE, and at developing new and original services that can be useful for the community. The noise data that are acquired by volunteers around the world (citizen observations), are organized in three files, containing the path of measures (a set of points), standardized noise indicators, noise description and other useful variables (GPS accuracy, speed…). These data can be very relevant later to propose an environmental noise evaluation, through simple or complex treatments.

International audience; Data quality assessment of different volunteered initiatives and platforms presents several challenges for data validation given the high amount of data collected. This paper focuses on two goals. The first consists in defining both a generic workflow and data quality indicators for validation of reports coming from crowd and community sourcing platforms. In the proposed workflow, a qualified report can be even described by each indicator separately or by a combination of them. Here, we focus mostly on analyzing the results obtained for each indicator separately. The second goal is to learn more information about contributors who has engaged in a platform proposed by a public body (i.e., the French National Mapping Agency): Who are they? How are they contributing? What are their motivations? More is known about contributors to OpenStreetMap than of any other VGI platform. Indeed, knowing the contributors is a crucial task for both motivation and data quality, especially now that public institutions are engaging with VGI.

International audience; The perspective of European National Mapping Agencies (NMA) on the role of citizen sensing in map production was explored. The NMAs varied greatly in their engagement with the community generating volunteered geographic information (VGI) and in their future plans. From an assessment of NMA standard practices, it was evident that much VGI was acquired with a positional accuracy that, while less than that typically acquired by NMAs, actually exceeded the requirements of the nominal data capture scale used by most NMAs. Opportunities for VGI use in map revision and updating were evident, especially for agencies that use a continuous rather than cyclical updating policy. Some NMAs had also developed systems to engage with citizen sensors and examples are discussed. Only rarely was VGI used to collect data on features beyond the standard set used by the NMAs. The potential role of citizen sensing and so its current scale of use by NMAs is limited by a series of concerns, notably relating to issues of data quality, the nature and motivation of the contributors, legal issues, the sustainability of data source, and employment fears of NMA staff. Possible priorities for future research and development are identified to help ensure that the potential of VGI in mapping is realized.

Le projet Mobitourgéo pose la question du potentiel de diverses données issues de la foule pour aider à analyser et quantifier les pratiques de mobilité des touristes et résidents. Les données issues des opérateurs téléphoniques sont particulièrement ciblées par le projet. Mais, plus largement, d’autres sources d’information peuvent être utilisées : photos partagées sur le Web, tweets, traces de randonnées partagées sur des sites spécialisés, etc. L’objet de ce document est d’illustrer, à travers des exemples de la littérature, quelles études peuvent être faites à partir de ces données en relation avec la problématique de Mobitourgéo, et quelles questions ces données soulèvent.

poster; International audience; Modelling social interactions in volunteered geographic information projects requires defining what binds contributors together. In order to be as realistic as possible, instead of choosing one social aspect to study, we choose to build a multi-layered social network that contains several types of interaction between VGI contributors. The analysis of such a multigraph should allow the detection of communities and the definition of typical profiles of contributors. A use case on OpenStreetMap illustrates what inferences can be made about contributions based on their authors.

International audience; Since the advent of geoweb, the landscape of geographic information has been constantly invested by new technical, legal and organizational arrangements on one hand, and new data producers, on the other hand. In particular, these new producers are implementing the alternatives to conventional modes of production of spatial data, from the Open movement (open source, open data, open access, open culture, open hardware, etc.) : the OpenStreetMap (OSM) project has emerged in 2004 in this context, with its community of contributors. The object of the analysis is twofold in this article : to highlight the profiles, practices, commitment values of those who constitute the hard core of the OSM community in France and to discuss the underside of this new mapping factory, that operates between utopia and digital common.; Depuis l’avènement du géoweb, le paysage de l’information géographique n’a cessé d’être investi par de nouvelles dispositions techniques, juridiques, organisationnelles d’une part, façonné par de nouveaux producteurs de données d’autre part. Issus en particulier de la mouvance du Libre (open source, open data, open access, open culture, open hardware, etc.), ces nouveaux producteurs mettent en œuvre des solutions alternatives aux modes conventionnels de production de l’information géographique. C’est dans ce cadre qu’émerge en 2004 le projet OpenStreetMap (OSM) et sa communauté de contributeurs. L’objet de l’analyse est double dans cet article : donner à voir les profils, pratiques, valeurs d’engagement de celles et ceux qui constituent le noyau dur de la communauté OSM en France et discuter des dessous de cette fabrique cartographique d’un nouveau genre qui opère entre utopie et commun numérique.

In this conference, we present the NoiseCapture application and the OnoM@p Spatial Data Infrastructure, dedicated to noise purposes.This presentation take place within the European ENERGIC-OD project final conference (https://www.energic-od.eu/).

International audience; OpenStreetMap data comprise of very detailed (e.g. zebra crossing) and quite rough features (e.g. built-up area). But making large scale maps from data with inconsistent level of detail often blurs map comprehension. This paper explores the automatic harmonization of OpenStreetMap data for large scale maps, i.e. the process that transforms rough objects to make them consistent with detailed objects. A typology of the new operators that harmonization requires is presented and six algorithms that implement the operators are described. Experiments with these algorithms raise several research questions about automation, parametrization, or the level of abstraction of the transformation, which are discussed in the paper.

OGRS2016, Open Source Geospatial Research & Education Symposium, PEROUSE, ITALIE, 12-/10/2016 - 14/10/2016; The present paper proposes an ideal Spatial Data Infrastructure (SDI) dedicated to noise monitoring based on volunteers measurements. Called OnoM@P, it takes advantage of the geospatial standards and open source tools to build an integrated platform to manage the whole knowledge about a territory and to observe its dynamics. It intends also to diffuse good practices to organize, collect, represent and process geospatial data in field of acoustic researches. OnoM@p falls within the framework of the Environmental Noise Directive (END) 2002/49/CE. The system relies on the NoiseCapture Android application developed for allowing each citizen to estimate its own noise exposure with its smartphone and to contribute to the production of community noisemaps.

International audience; The paper presents empirical research on the quality of the toponyms that can be retrieved from OpenStreetMap (OSM) under the purpose of enriching authoritative toponymic databases and gazetteers. An analysis on the volatility of places and points-of-interest (POIs) is presented. We examine how named features behave and change in terms of type, name and location. The challenge is to understand the behavior and consequently the fitness-for-purpose of OSM data when it comes to a possible use and integration with authoritative datasets. We show that, depending on the OSM feature type, the volatility can vary considerably and we elucidate which feature types are consistent, and thus could be used in authoritative gazetteers despite their grassroots nature and if there are spatial patterns behind the location changes of features during their lifespan.

International audience; Traditionally, national mapping agencies produced datasets and map products for a low number of specified and internally consistent scales, i.e. at a common level of detail (LoD). With the advent of projects like OpenStreetMap, data users are increasingly confronted with the task of dealing with heterogeneously detailed and scaled geodata. Knowing the scale of geodata is very important for mapping processes such as for generalization of label placement or land-cover studies for instance. In the following chapter, we review and compare two concurrent approaches at automatically assigning scale to OSM objects. The first approach is based on a multi-criteria decision making model, with a rationalist approach for defining and parameterizing the respective criteria, yielding five broad LoD classes. The second approach attempts to identify a single metric from an analysis process, which is then used to interpolate a scale equivalence. Both approaches are combined and tested against well-known Corine data, resulting in an improvement of the scale inference process. The chapter closes with a presentation of the most pressing open problems

International audience; Volunteered Geographic Information (VGI) represents a valuable source of information. At the same time this data presents new data quality issues such as credibility. In this paper, we investigate the integration of VGI in Spatial OLAP (SOLAP) systems, which allow analyzing huge volume of geographic datasets. By means of a real case study, we highlight the similarities and differences of these two kinds of systems. We propose a methodology to handle VGI data quality issues during the warehousing and OLAPing phases. In particular, we define a new aggregation ETL operator based on the VGI credibility. We also present a spatio-multidimensional model that provides decision-makers with a general description of the correctness of the SOLAP aggregations.

poster; International audience

National audience; Les entrepôts de données spatiales et les systèmes SOLAP permettent l’analyse en ligne de gros volumes de données géo-référencées. Dans un contexte de gestion des risques naturels, ces systèmes ont été utilisés avec succès sur des données officielles dont la collecte suit un processus bien établi. Le développement récent d’outils collaboratifs pour la saisie de données géographiques, y compris par des utilisateurs non-experts a engendré un nouveau concept : celui de l’Information Géographique Volontaires (VGI), et a créé un besoin et une opportunité pour prendre en compte ces données dans un processus décisionnel. Dans cet article, nous décrivons un modèle et un système patiomultidimensionnel permettant l’utilisation conjointe des données VGI avec des systèmes SOLAP pour l’analyse des aléas environnementaux.

International audience; Research on generalisation algorithms mainly focused on the most prominent themes of a topographic map, namely roads, buildings, hydrography, terrain and land use. Now that many efficient algorithms exist for such prominent themes (Stanislawski et al. 2014), minor themes like ditches (Savino et al. 2011) or swamp patches (Anderson - Tarver et al. 2011) gain interest from the research community. In OpenStreetMap (OSM), the loose specifications resulted in a large range of unusual themes, e.g. electric lines (Figure 1c), and generalising maps out of OSM data requires to handle such unusual themes. Moreover, another particularity of OSM is the very high level of detail that can be found for some features, compared to traditional geographical databases, e.g. airports, port sites (Figure 1a) or parks (Figure 1b). The unusual details contained in such areas also require specific algorithms to preserve the structure and the relations between the components. Generalising OSM data is a challenging task (Sester et al. 2014) but this paper only focuses on the generalisation of those map themes or areas exhibited in OSM.

International audience; Whereas it was possible to define the level of detail (LoD) of authoritative datasets, it is not possible for Volunteered Geographic Information (VGI), often char-acterised by heterogeneous levels of details. This heterogeneity is a curb for map-making, particularly when using traditional map derivation processes such as gener-alisation. The paper proposes a method to infer the level of detail of VGI features. Then, inconsistencies between features with different levels of detail that get in the way of good mapmaking can be automatically identified. Some proposals are made to harmonise level of detail heterogeneities. The LoD inference is implemented and results are presented on OpenStreetMap data.

Part 2: eEnvironment and Cross-Border Services in Digital Agenda for Europe; International audience; We investigate the potential role Volunteered Geographic Information (VGI) can play in eEvironment and various Spatial Data Infrastructures (SDI) on a local, regional, and national level. eEnvironment is the use and promotion of ICT for the purposes of environmental assessment and protection, spatial planning, and the sustainable use of natural resources. An SDI provides an institutionally sanctioned, automated means for posting, discovering, evaluating, and exchanging geospatial information by participating information producers and users. A key common theme shared by both definitions is public participation and user-centric services. We pose the research question: is VGI (an example of public participation and collaboration) is ready to participate in eEnvironment and SDI?

International audience; The concept of Volunteered Geographic Information (VGI) has recently emerged from the new Web 2.0 technologies. The OpenStreetMap project is currently the most significant example of a system based on VGI. It aims at producing free vector geographic databases using contributions from Internet users. Spatial data quality becomes a key consideration in this context of freely downloadable geographic databases. This article studies the quality of French OpenStreetMap data. It extends the work of Haklay to France, provides a larger set of spatial data quality element assessments (i.e. geometric, attribute, semantic and temporal accuracy, logical consistency, completeness, lineage, and usage), and uses different methods of quality control. The outcome of the study raises questions such as the heterogeneity of processes, scales of production, and the compliance to standardized and accepted specifications. In order to improve data quality, a balance has to be struck between the contributors' freedom and their respect of specifications. The development of appropriate solutions to provide this balance is an important research issue in the domain of user-generated content.

National audience; Les nouvelles technologies du Web 2.0 ont permis l'émergence des contenus créés collaborativement dont l'exemple le plus intéressant est le projet OpenStreetMap. Ce projet vise à produire une base de données topographiques entièrement libre. Cet article étudie la qualité des données françaises de ce projet en prenant comme comparaison des données de l'Institut Géographique National. La qualité des données est très hétérogène du fait de l'absence de spécifications précises et acceptées. ABSTRACT. New concepts like free data or Volunteered Geographic Information recently emerged thanks to new Web 2.0 technologies. The OpenStreetMap project is the most significant example. It aims at producing free vector topographic databases. This paper studies the OpenStreetMap french data quality compared to institutional databases of the french NMA. Data Quality is quite heterogeneous due to a lack of precise and well-accepted specifications.