Ontology of Topographic Eminences for the National Map

2018

The landform reference ontology (LFRO) formalizes ontological distinctions underlying naïve geographic cognition and reasoning about landforms. The LFRO taxonomy is currently based only on form-based distinctions. In this significantly revised version, several new categories have been added to explicate ontological distinctions related to material-spatial dependence and physical support. Nuances of common natural language landform terms and implications for their mapping are discussed. 2012 ACM Subject Classification Information systems → Ontologies

2017

This reference landform ontology is intended to guide automated delineation of landforms from digital elevation models (DEMs) and semantic information retrieval about landforms. Since only form related information is available from DEMs, the categories of this reference ontology are defined based only on morphological criteria. The choice of the landform categories is informed by ethnophysiographic and spatial cognition research. The proposed taxonomy is work in progress and reflects the current focus on automated delineation and mapping of depression landforms (e.g., basins, valleys and canyons).

2003

Ethnophysiography is a new term coined by the authors to refer to an ethnoscience of landforms. Ethnophysiography seeks to document and compare terms used in various languages and cultures to refer to the natural landscape and its parts, and the meanings of those terms. Ethnophysiography is an important part of efforts to construct ontologies of the geographic domain because the categories of landforms, water bodies, etc. are not clearly differentiated in nature the way terms for kinds of plants and animals typically are. Landscape terms and their meanings appear to subdivide reality differently in different cultures; thus clear definitions of meanings in an ontological framework with universal expressive power are needed if semantic interoperability across languages and cultures is to be achieved.

2007

This paper reports on the design of a semantic data model that integrates topographical databases at different map-scales at machine level in order to facilitate the (semi-)automated generalisation process (generation of small scale databases from large scale databases). Apart from the data content to produce coherent topographical databases at the different scales (object classes, attributes, attribute values, relationships and constraints), the data model should contain information to produce coherent topographical maps at different scales. The context, requirements, possible alternatives and criteria to be fulfilled by the semantic data model are presented. From the first experiments it can be concluded that for modelling data content covering all scales, a UML class diagram that starts with topographical object classes that occur at any scale looks most promising. These object classes are defined as superclasses and subclasses are created at the moment specific attributes or attribute values, relationships or constraints are only applicable for a specific scale. Future research will focus on how the consistency of geometry, topology and generalisation related spatial structures can be defined in the UML class diagram. In addition it will be explored how cartographic constraints can be defined in the UML <<interface>> object which is created for every map product that has to be produced.

We describe our efforts to use the Theory of Institutions, from Goguen and Burstall[1], as a unifying mechanism for constructing models from the diverse elements present in topographic information. The informal setting and main objectives are outlined.

Computers & Geosciences, 2001

Landforms, which result from the interplay of physical, chemical, and biological processes acting on the surface, function as static boundary conditions for processes in geomorphology, hydrology, meteorology and other fields. The description, parameterization, and modeling of landform structure, as well as the terminology used, are fitted to the requirements of the disciplines and are, therefore, often strongly divergent. As a consequence, representations of landform structure for different disciplines are often not compatible and require frequent revisions and adaptations. Principles of the semantic approach to the problem are presented in this paper. The main objective is a semantically correct description of landform which is useful to all disciplines related to surface structure. The approach considers geometric form as a basic property, extended by topological considerations and semantic definitions. The potential, limitations, and open questions of the semantic-based approach are discussed using hillslopes as a case study. The focus of the paper is on semantic representation and only thereafter are the special features of DEMs, tools, and implementations considered. #

2000

In order to understand how people see the world and how, ultimately, mental conceptualizations of the comprehended geographic features are represented in a computer system, we must develop abstraction paradigms. The result of the abstraction process is a general view of the process from the real object to its computer representation. Different levels of abstraction allow the development of specific tools for the different types of problems at each level. Creating a solid conceptual model is at the foundation of system design practice. Lately, ontologies were brought into the discussion on modeling. For instance, Guarino (1998, p. 10) says that "every (symbolic) information system (IS) has its own ontology, since it ascribes meaning to the symbols used according to a particular view of the world." Wand and Weber (2004, p. v) argue that since theories of ontology are tools that help us describe a specific world (i.e., the target of an IS), "our information systems will only be as good as our ontologies."

2004

This paper describes the initial thoughts and experiences of Ordnance Survey in the development of a topographical ontology. It describes the construction of a subset of a full topographical ontology, one restricted to inland water. The paper also describes our approach to the construction of such an ontology, one which attempts to maximise the ability to move shared concepts between ontologies. Lastly, it describes the issues raised when attempting to represent the ontology in DAML+OIL.

ISO/TC 211/WG 7/PT 19150 Expected action: For information and consideration at the 28th ISO/TC 211 plenary meeting in Molde, NorwayISO/ ISO TC 211/SC /WG 7 Secretariat: SN Geographic information -Ontology -Project 19150 -Preliminary stage (00.60) Information géographique -Ontologie -Projet 19150 -Stade préliminaire (00.60)

The integration of topographic data sets is defined as the process of establishing relationships between corresponding object instances in different, autonomously produced, topographic data sets of the same geographic space. The problem of integrating topographic data sets is in finding these relationships, considering the differences in content and abstraction. A conceptual framework is developed. Components of this framework are ontologies and sets of surveying rules. New in this approach is the introduction of a reference model. A reference model belongs uniquely to the combination of topographic data sets to be integrated. The framework is tested on two topographic data sets with area instances (polygons) which have crisp and complete boundaries and are not displaced for cartographic reasons. The overall conclusion is that the ontology-based framework is feasible, if (1) there is (at least partial) knowledge of the surveying rules, and (2) the data sets can be synchronized in time. The application of this framework is most suitable for object classes with instances that are easy to identify and have a limited spatial extent (e.g., buildings).

Computers &amp; Geosciences, 2018

This paper introduces a novel, semantics-informed geologic mapping process, whose application domain is the production of a synthetic geologic map of a large administrative region. A number of approaches concerning the expression of geologic knowledge through UML schemata and ontologies have been around for more than a decade. These approaches have yielded resources that concern specific domains, such as, e.g., lithology. We develop a conceptual model that aims at building a digital encoding of several domains of geologic knowledge, in order to support the interoperability of the sources. We apply the devised terminological base to the classification of the elements of a geologic map of the Italian Western Alps and northern Apennines (Piemonte region). The digitally encoded knowledge base is a merged set of ontologies, called OntoGeonous. The encoding process identifies the objects of the semantic encoding, the geologic units, gathers the relevant information about such objects from authoritative resources, such as GeoSciML (giving priority to the application schemata reported in the INSPIRE Encoding Cookbook), and expresses the statements by means of axioms encoded in the Web Ontology Language (OWL). To support interoperability, OntoGeonous interlinks the general concepts by referring to the upper part level of ontology SWEET (developed by NASA), and imports knowledge that is already encoded in ontological format (e.g., ontology Simple Lithology). Machine-readable knowledge allows for consistency checking and for classification of the geological map data through algorithms of automatic reasoning.

Cartographica: The International Journal for Geographic Information and Geovisualization, 2010

Terrain is generally stored in GIS as an elevation field, whereas human cognition of the landscape is usually object based. To address this mismatch of terrain data models, we propose object-based terrain representation, using topographic eminences, which are landforms that rise up conspicuously from the ground to visibly dominate the landscape, to illustrate our case. We propose a cognition-based methodology for automated detection and delineation of eminences from digital elevation models (DEMs). Alternative conceptualizations of the landscape can be realized by simple manipulation of intuitive parameters such as a peak&#39;s relative height and distance. Our approach delimits the extent of eminences based purely on topographic gradient and aspect, much like the delineation of ridges as watershed boundaries. Smaller eminences can be incrementally aggregated into larger cognitive wholes, enabling scale-sensitive landscape reconstruction. The ability to integrate field and object vi...

Why landscape terms matter for mapping: A comparison of ethnogeographic categories and scientific classification., 2014

International Journal of Geographical Information Science, 2011

National mapping agencies maintain topographic data sets at different scales. Keeping the data sets consistent, for example by means of automated update propagation, requires formal knowledge on how the different data sets relate to each other. This article presents a multi-scale information model that, first, integrates the data states at the different scales and, second, formalises semantics on scale transitions. This is expressed using the Unified Modelling Language (UML) class diagrams, complemented with Object Constraint Language (OCL). Based on a requirement analysis using the needs of the Netherlands' Kadaster as case study, this article examines several modelling alternatives and selects the optimal modelling approach for a multi-scale information model for topography. The model is evaluated through a prototype database implementation. The results show that UML/OCL provides an appropriate formalism to model rich semantics on both multi-scale data content and scale transitions, which can be used for guarding consistency based on automated generalisation of updates. Further research is required to express generalisation specifications that are currently not formalised and that are only available in software code or as cartographers' knowledge.

Journal of Spatial Information Science, 2020

Over the past 50 years or so the representation of spatial information within computerized systems has been widely addressed and developed in order to provide suitable data manipulation, analysis, and visualisation mechanisms. The range of applications is unlimited and nowadays impacts almost all sciences and practices. However, current conceptualisations and numerical representations of geospatial information still require the development of richer abstract models that match the complexity of spatial and temporal information. Geospatial ontologies are promising modelling alternatives that might favour the implementation and sharing of geographical information. The objective of this vision paper is to provide a short introduction to the principles behind semantic ontologies and how they can be applied to complex geospatial information, by evaluating their potential and limitations.