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Wayfinding in a natural setting is one of the many complex processes human beings face when acting in the environment. Despite recent developments and applications of wayfinding in urban environments, little research has been oriented and applied to natural environments. The research presented in this article introduces an ontological and language-based modelling of human navigation in a natural setting. The experimental approach was applied to a foot orienteering race that has the advantage of being semantically rich and combined with cartographic support, enabling the study of the importance of landmarks, actions, and the role of the underlying nature of the land and topography. Experimental results are compared to those of studies conducted in urban environments and permit the identification of similarities and differences between wayfinding descriptions made in urban contexts and those made in natural contexts. RésuméLes déplacements en milieu naturel sont des processus complexes auxquels les humains doivent faire face quand ils interagissent dans un environnement. Malgré les nouveaux développements et les nouvelles applications de navigation en milieu urbain, peu de recherches ont été menées en environnement naturel. Cet article introduit un modèle ontologique combiné à un langage de description d'itinéraires en milieu naturel. Cette démarche a été expérimentée dans un contexte de course d'orientation. Ce type d'activité a l'avantage d'être sémantiquement riche, et associé à un support cartographique. Une telle approche permet d'étudier l'importance des points de repère, des actions et des rôles respectifs de la nature du terrain et de la topographie. Les résultats expérimentaux sont comparés à des études menées en environnement urbain. Cette analyse permet de distinguer les similarités et les différences entre les descriptions d'itinéraire réalisées en milieu urbain et naturel.Mots clés : processus de navigation, environnement naturel, description d'itinéraire

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The modeling of a landscape environment is a cognitive activity that requires appropriate spatial representations. The research presented in this paper introduces a structural and semantic categorization of a landscape view based on panoramic photographs that act as a substitute of a given natural environment. Verbal descriptions of a landscape scene provide the modeling input of our approach. This structure-based model identifies the spatial, relational and semantic constructs that emerge from these descriptions. Concepts in the environment are qualified according to a semantic classification, their proximity and direction to the observer, and the spatial relations that qualify them. The resulting model is represented in a way that constitutes a modeling support for the study of environmental scenes, and a contribution for further research oriented to the mapping of a verbal description onto a geographical information system-based representation.

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A landform is a subjective individuation of a part of a terrain. Landform recognition is a difficult task because its definition usually relies on a qualitative and fuzzy description. Achieving automatic recognition of landforms requires a formal definition of the landforms properties and their modelling. In the maritime domain, the International Hydrographic Organisation published a standard terminology of undersea feature names which formalises a set of definition mainly for naming and communication purpose. This terminology is here used as a starting point for the definition of an ontology of undersea features and their automatic classification from a terrain model. First, an ontology of undersea features is built. The ontology is composed of an application domain ontology describing the main properties and relationships between features and a representation ontology deals with representation on a chart where features are portrayed by soundings and isobaths. A database model was generated from the ontology. Geometrical properties describing the feature shape are computed from soundings and isobaths and are used for feature classification. An example of automatic classification on a nautical chart is presented and results and ongoing research are discussed.