Knowledge formalization for vector data matching using belief theory

Nowadays geographic vector data is produced both by public and private institutions using well defined specifications or crowdsourcing via Web 2.0 mapping portals. As a result, multiple representations of the same real world objects exist, without any links between these different representations. This becomes an issue when integration, updates, or multi-level analysis needs to be performed, as well as for data quality assessment. In this paper a multi-criteria data matching approach allowing the automatic definition of links between identical features is proposed. The originality of the approach is that the process is guided by an explicit representation and fusion of knowledge from various sources. Moreover the imperfection (imprecision, uncertainty, and incompleteness) is explicitly modeled in the process. Belief theory is used to represent and fuse knowledge from different sources, to model imperfection, and make a decision. Experiments are reported on real data coming from different producers, having different scales and either representing relief (isolated points) or road networks (linear data)

Référencement spatial indirect: modélisation à base de relations et d’objets spatiaux vagues

International audienceThis paper proposes a methodology for transforming a relative position description expressed in an indirect repository into an absolute position in a direct repository. This problem requires the formalization of relative location elements, here in the form of a triplet model, which is used as a basis for the transformation from relative to absolute position and the questioning on how the imperfection of the natural language in the modeling process can be taken into account. We propose to use fuzzy spatial objects and we will compare their implementations to identify the most appropriate approach to our context.Cet article propose une méthodologie destinée à transformer une description de position exprimée dans un référentiel indirect en une position absolue dans un référentiel direct.Cette problématique nécessite une formalisation des éléments de localisation relatifs, ici sous la forme d’un modèle en triplets, utilisés comme base de référencement et un questionnement sur la prise en compte de l’imperfection du langage naturel dans le processus de modélisation. Nous proposons de faire appel aux objets spatiaux flous et nous comparerons leurs implémentations afin d’identifier l’approche la plus adaptée au contexte du secours en montagne

Assuring the quality of VGI on land use and land cover: experiences and learnings from the LandSense project

The potential of citizens as a source of geographical information has been recognized for many years. Such activity has grown recently due to the proliferation of inexpensive location aware devices and an ability to share data over the internet. Recently, a series of major projects, often cast as citizen observatories, have helped explore and develop this potential for a wide range of applications. Here, some of the experiences and learnings gained from part of one such project, which aimed to further the role of citizen science within Earth observation and help address environmental challenges, LandSense, are shared. The key focus is on quality assurance of citizen generated data on land use and land cover especially to support analyses of remotely sensed data and products. Particular focus is directed to quality assurance checks on photographic image quality, privacy, polygon overlap, positional accuracy and offset, contributor agreement, and categorical accuracy. The discussion aims to provide good practice advice to aid future studies and help fulfil the full potential of citizens as a source of volunteered geographical information (VGI)

Considerations of Privacy, Ethics and Legal Issues in Volunteered Geographic Information

International audienceToday almost any kind of User Generated Content (UGC) can be situated within a geographic context. Volunteered Geographic Information (VGI) can include many types of UGC, such as georeferenced photographs, social media and text, geographic data themselves, etc. There are legal, privacy and ethical issues raised by VGI, and at present these are not very well studied or understood despite the rise in popularity of VGI. This chapter will discuss, investigate and define some 120 Mapping and the Citizen Sensor of the most prominent issues related to the legal, privacy and ethics topic within VGI. The chapter argues that these issues are not well understood by all of the actors in VGI, and in particular by the producers of this information as well as the users or consumers of this new data source. Creating a better understanding of these issues will be very important in the future development and evolution of VGI in society

Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information

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

Mapping and the Citizen Sensor

The role of citizens in mapping has evolved considerably over the last decade. This chapter outlines the background to citizen sensing in mapping and sets the scene for the chapters that follow, which highlight some of the main outcomes of a collaborative programme of work to enhance the role of citizens in mapping