Now showing 1 - 6 of 6
  • Publication
    Inferring Semantics from Geometry - the Case of Street Networks
    This paper proposes a method for automatically inferring semantic type information for a street network from its corresponding geometrical representation. Specifically, a street network is modelled as a probabilistic graphical model and semantic type information is inferred by performing learning and inference with respect to this model. Learning is performed using a maximum-margin approach while inference is performed using a fusion moves approach. The proposed model captures features relating to individual streets, such as linearity, as well as features relating to the relationships between streets such as the co occurrence of semantic types. On a large street network containing 32,412 street segments, the proposed model achieves precision and recall values of 68% and 65% respectively. One application of this work is the automation of street network mapping.
      583
  • Publication
    Automated Highway Tag Assessment of OpenStreetMap Road Networks
    OpenStreetMap (OSM) has been demonstrated to be a valuable source of spatial data in the context of many applications. However concerns still exist regarding the quality of such data and this has limited the proliferation of its use. Consequently much research has been invested in the development of methods for assessing and/or improving the quality of OSM data. However most of these methods require ground-truth data, which, in many cases, may not be available. In this paper we present a novel solution for OSM data quality assessment that does not require ground-truth data. We consider the semantic accuracy of OSM street network data, and in particular, the associated semantic class (road class) information. A machine learning model is proposed that learns the geometrical and topological characteristics of di erent semantic classes of streets. This model is subsequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.
      867Scopus© Citations 44
  • Publication
    Cognitively Adequate Topological Robot Localization and Mapping
    Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the eld of robotics which concerns mapping an environment or space while simultaneously localizing within this map. Given that one of the major goals of robotics is to perform tasks commonly performed by humans, we argue that SLAM methods should be cognitively adequate; that is, they should model the same properties of a space as the human cognition models. Topological properties are considered the most fundamental of those modlelled by the human cognition. Therefore in order to achieve cognitive adequacy such properties must be modelled explicitly. Research in the domain of spatial cognition has demonstrated that the topological properties modelled by the human cognition can be quanti ed using the Egenhofer Nine-Intersection Model (9-IM). In this work we propose a conceptual SLAM method which models the same properties as the 9-IM. Relative to existing topological SLAM methods, which model a single topological property of connectivity between locations, this method achieves a stronger degree of cognitive adequacy.
      424
  • Publication
    Utilizing geometric coherence in the computation of map transformations
    Adaptive mapping and real-time spatial data delivery are currently major research topics in the fields of Web-GIS and Location Based Services (LBS). In order to successfully implement these paradigms a methodology to progressively adopt or transform an arbitrary map representation in an arbitrary fashion on-the-fly is necessary. Such a methodology was previously considered not feasible due to the high computational complexity associated with existing automated map generalisation methodologies. This paper presents a methodology, inspired by related research in the field of computer graphics, which offers the potential to reduce such associated computational complexity. This is achieved using an approach to map transformation which takes advantage of the geometric coherence between a user's spatial data requirements.
      506Scopus© Citations 1
  • Publication
    Interactive cartographic route descriptions
    Providing an adequate route description requires in-depth spatial knowledge of the route in question. In this article we demonstrate that despite having travelled a route recently and having much experience of the area in question, an individual may lack such a degree of knowledge. Previous research and experience informs us that a map is an effective tool for bridging gaps in one’s spatial knowledge. In this article we propose an approach, known as an Interactive Route Description, for defining and interpreting route descriptions interactively with a map. This approach is based on the concept of annotating the map in question and allows the aforementioned gap in one’s spatial knowledge to be bridged. An additional benefit of defining route descriptions in this way is that it facilitates automatic parsing and in turn offers many potential applications. One such application, illustrated in this paper, is the automatic transformation to other representations of the description such as turn-by-turn instructions.
      420Scopus© Citations 5
  • Publication
    Characterising the metric and topological evolution of OpenStreetMap network representations
    (Springer, 2013-01) ;
    OpenStreetMap (OSM) is a collaborative project to create a free editable map database of the world. This paper presents an analysis of the evolution of OSM street network representations. Three urban areas in Ireland were analysed where each evolves from containing little street network detail to a highly detailed street network. In order to characterise this evolution a number of metric and topological characteristics were computed. Some characteristics exhibited broadly similar behaviour in each region. This may be a attributed to similarities in the degree of contributor activity and intrinsic universal mapping procedures exhibited by contributors
      561Scopus© Citations 28