Rough classification and accuracy assessment

Ahlqvist, Ola; Keukelaar, Johannes; Oukbir, Karim

doi:10.1080/13658810050057605

Cited by 75 publications

(30 citation statements)

References 14 publications

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“…Approaches in this category include earlier fuzzy regions [3], the formal definition of fuzzy points, fuzzy lines and fuzzy regions in [23], and an extension of the rough classification from [1] to account for fuzzy regions [2]. A recent effort for the definition of a spatial algebra based on fuzzy sets is presented in [9].…”

Section: Related Workmentioning

confidence: 99%

“…Although fundamentally different to the exact based approaches, rough set theory [22] provides tools for deriving concepts with a close relation to what can be achieved with exact models. Rough set theory based approaches include early work by Worboys in [26], the concepts for deriving quality measures presented in [4], and the concept of rough classification in [1].…”

Section: Related Workmentioning

confidence: 99%

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Querying Vague Spatial Objects in Databases with VASA

Pauly

2008

Quality Aspects in Spatial Data Mining

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Recent years have been witness to the increasing efforts of scientists to design concepts and implementations that can adequately handle the vagueness and imprecision that is widespread in spatial data. Plenty of spatial entities, especially those that occur naturally such as mountains and biotopes, contain intrinsic vague attributes that make their representation as crisply bounded entities ineffective and far from exact. Other examples of such spatial objects include population density, pollution clouds, oil pouches, and even lakes and rivers whose water levels are not determinate but rather can change depending on the pluvial activity. In the context of spatial databases, retrieval and handling of vague spatial objects through querying is critical in exploiting the functionality of the database. Thus, it is important that query mechanisms are able to handle the vagueness that is included in the data models used to represent the objects that are stored. In this paper we present a Vague Spatial Algebra (VASA) capable of handling spatial vagueness. VASA is defined on the basis of exact models for crisp spatial objects and includes the definitions of vague spatial data types as well as the operations and predicates needed to effectively manipulate instances of these data types. We introduce the appropriate concepts for enabling database querying that exploits the power of the vague spatial data model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Querying Vague Spatial Objects in Databases with VASA

Pauly

2008

Quality Aspects in Spatial Data Mining

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“…Many statistical techniques common in GIS, such as Heuvelink's analysis and the classification error matrix or CEM [14], are ultimately based on the analysis of inconsistencies between two data sets, one of which is usually an independent data source of higher accuracy. While the most common techniques for inconsistency resolution in GIS are quantitative, qualitative techniques are increasingly important, for example based on belief revision (e.g., [15]), fuzzy sets (e.g., [16,17]), or rough sets (e.g., [18]). …”

Section: Inconsistency Resolutionmentioning

confidence: 99%

Qualitative reasoning about consistency in geographic information

Duckham

Lingham

Mason

et al. 2006

Information Sciences

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This paper explores the development and use of a qualitative reasoning system for describing consistency between different geographic data sets. Consistency is closely related to issues of uncertainty and interoperability in geographic information, and the paper assesses how automated reasoning about consistency can be used to support the integration of heterogeneous geographic data sets. The system developed is based on description logic. The decidability and tractability characteristics of description logic allow consistency checking to be deferred during data integration, so minimizing the information loss that usually accompanies any data integration task. Further, the user interface allows users to negotiate with the system in defining consistency. The results of the research suggest that further work could significantly increase the level of automation for many geographic data integration tasks.

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“…Ahlqvist et al (2000) used rough set theory to manage uncertainty between spatially coincident but semantically and conceptually divergent data. An uncertain set is specified using an upper and a lower approximation to indicate extremes of certitude.…”

mentioning

confidence: 99%

Integrating land-cover data with different ontologies: identifying change from inconsistency

Comber

Fisher²,

Wadsworth³

2004

International Journal of Geographical Information Science

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Spatially coincident land cover information frequently varies due to technological and political variations. This is especially problematic for time-series analyses. We present an approach using expert expressions of how the semantics of different datasets relate to integrating temporal time series land cover information where the classification classes have fundamentally changed. We use LCMGB and LCM2000 as example data sets because of the extensive object based meta-data in the LCM2000. Inconsistencies between the two datasets can arise from random, gross and systematic error and from an actual change in land cover. Locales of possible land cover change are inferred by comparing characterisations derived from the semantic relations and meta-data. Field visits showed errors of omission to be 21% and errors of commission to be 28%, despite the accuracy limitations of the land cover information when compared to the field survey component of the Countryside Survey 2000. IntroductionThe process of natural resource inventory takes place against a background of change which may result in inconsistency. Human understanding within the scientific field which is the subject of the inventory may well vary as may the policy initiative under which the inventory occurs. The methods by which the inventory could be conducted may be revised and the nature of the resource at a location may change (Comber et al., 2003a). These different changes necessitate the re-inventory of an area after some number of years. In geological mapping and soil survey changes in understanding and methods are the most important reasons for re-inventory. In the study of land use and land cover the main incentive for remapping is that the use or cover itself may have changed, but at the same time there may be alterations to the methods of analysis. Changes in methodology make it difficult to separate changes in the phenomenon being measured (such as land cover) from changes that are the result of the revised methodology; that is to say ontological inconsistency may be a problem for change detection. This discord causes problems for research that seeks to develop time-series of land cover or land use to 2 monitor environmental change and for initiatives that aim to react to environmental change.The issue of dataset inconsistency is endemic to resource inventory and should be unsurprising to many involved in the activity. Firstly, different surveys at the same instance in time may record nominally similar features (such as land cover), but may do so in completely different ways due to their particular institutional or, indeed, personal perspectives. Secondly, different surveys at different points in time would not be expected to record objects of interest (such as patches of land cover) in the same way because of scientific developments and new policy objectives (Comber et al., 2002;2003a).The effect of changing methodologies is that much of the value of previous land resource inventories is lost with each successive survey; each inventory become...

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Rough classification and accuracy assessment

Cited by 75 publications

References 14 publications

Querying Vague Spatial Objects in Databases with VASA

Querying Vague Spatial Objects in Databases with VASA

Qualitative reasoning about consistency in geographic information

Integrating land-cover data with different ontologies: identifying change from inconsistency

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