An Ontology for Submarine Feature Representation on Charts

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 on-going research are discussed

Multi-level representation of terrain features on a contour map

Contour lines are important for quantitatively displaying relief and identifying morphometric features on a map. Contour trees are often used to represent spatial relationships between contours and assist the user in analysing the terrain. How- ever, automatic analysis from the contour tree is still limited as features identified on a map by sets of contours are not only characterised by local relationships be- tween contours but also by relationships with other features at different levels of representation. In this paper, a new method based on adjacency and inclusion re- lationships between regions defined by sets of contours is presented. The method extracts terrain features and stores them in a feature tree providing a description of the landscape at multiple levels of detail. The method is applied to terrain analysis and generalisation of a contour map by selecting the most relevant features accord- ing to the purpose of the map. Experimental results are presented and discussed

Identification automatique des vallées glaciaires à partir d'un modèle numérique de terrain

La vallée glaciaire est une forme de relief associée aux montagnes qui ont été affectées par une glaciation régionale. L’étude de cette forme de relief aide les chercheurs à mieux connaitre le développement du système glaciaire régional qui reflète les changements climatiques globaux. La tâche d’identification de ces formes sur un modèle numérique de terrain est primordiale pour toutes les études géomorphologiques ou environnementales. Avec le développement desSystèmes d’Information Géographique et des outils informatiques, l’automatisation de cette tâche devient une solution pour la diminution de la durée et du coût exigés par la méthode manuelle. Cependant, les méthodes automatiques actuelles comme la classification morphométrique ou l'analyse d’images par objets (Object Based Image Analysis) sont limitées pour l'identification d'une forme de relief entière comme la vallée glaciaire. Ces méthodes sont basées en général sur des approches de classification du terrain en éléments de relief sans la prise en considération du contexte géomorphologique. À cet égard, nous avons comme objectif le développement d'une méthode automatique d'extraction des formes des vallées glaciaires. Notre méthode est basée sur l’idée de la saillance issue de la cognition humaine en matière des formes de relief. Nous admettons que la vallée glaciaire est définie par l’assemblage spatial du fond et des versants dans un contexte géomorphologique donné. L’identification de ces formes est réalisée à partir de l’identification des cours d’eau et des contreforts comme éléments saillants de ces composants et par leur assemblage autour d’une saillance globale, le talweg. Nous avons appliqué notre méthode automatique sur des modèles numériques de terrain de différentes zones glaciaires parmi lesquelles la vallée Jacques-Cartier au Québec. Nous avons obtenu des entités spatiales qui caractérisent l'étendue géographique de la vallée glaciaire et de ses éléments composants accompagnées de leurs indicateurs de forme.The glacial valley is a landform associated with mountains that have been affected by regional glaciation. The study of this kind of valley helps researchers to better understand the development of the regional glacial system that reflects global climate change. The task of identifying these forms on a digital elevation modelis essential for all geomorphological or environmental studies. With the development of GIS and IT tools, the automation of this task becomes a solution for reducing the time and cost required by the manual method.However, current automatic methods like morphometric classification or Object Based Image Analysis are limited when trying to identify an entire landform such as the glacial valley. These methods are generally based on land classification approaches of terrain elements without taking into consideration the geomorphological context. In this respect, we aim to develop an automatic method for extracting glacial valleys. Our method is based on the idea of salience resulting from human cognition of relief. We admit that the glacial valley is defined by the spatial assembly of the bottom and slopes in each geomorphological context. The identification of these forms is based on the identification of streams and foothills as salient elements of its components and their assembly around a global salience, the thalweg. We applied our automatic method on digital terrain models of different glacial zones, including the Jacques-Cartier Valley in Quebec. We obtained spatial entities that characterize the geographic extent of the glacial valley and its component elements accompanied with form indicators

Computation and elicitation of valleyness

Existing methods for land surface form characterisation often focus on relatively simple landform element classifications and do not evaluate results with large scale participant experiments. This piece of research takes a semantics-grounded approach to characterising the landform valley. Based on definitions three algorithms to characterise valleys in a fuzzy way are introduced. Comparison of the results to assessments regarding the degree of being in a valley gained from over 800 participants in a questionnaire survey yields significant amounts of explained variance (R2 D 0.35–0.37). Furthermore, accounting for very ambiguously perceived stimuli showing vast low places leads to markedly improved regressions (R2 D 0.45–0.49), weighting of the data with a measure of uncertainty in judgment even more so (R2 D 0.50–0.55)