IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)
Automatic categorization of large-scale topographic vector data into roads, buildings and similar... more Automatic categorization of large-scale topographic vector data into roads, buildings and similar classes typically examines each object description in isolation. We describe a Cartographic Structure Matching (CSM) algorithm that automatically classifies objects in topographic maps by examining the context of the object. Matching clusters of objects against known templates serves to categorize ambiguous polygons by including context in the categorization process. We describe a number of applications that emerged from the core structure-matching algorithm, addressing problems of error detection, rejoining partitioned objects, composite object identification and data quality estimation.
We describe the application of analogical structure matching to the problem of classifying object... more We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of these attributes from the sample data. Our domain-specific Cartographic Structure Matching Algorithm is then introduced and explained. The fusion of our algorithm's results with other classification techniques is mentioned, and some examples of the detection of misclassified polygons are provided. We finally provide a preliminary evaluation of our classification technique and suggest some future developments.
The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian... more The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian co-ordinatebased point, line and text label features within the tile-based Land-Line® Database. Under their Digital National Framework™ (DNF™) project, this data has been re-engineered into a topologically structured format known as OS MasterMap™ [Ordnance Survey]. This required the modelling of the area features enclosed by the line data as polygon objects. This new polygon-enriched data can be provided seamlessly for pre-defined areas and by theme. Each feature is assigned a unique Topographic Identifier (TOID™) number, allowing for the easy updating of a data holding, and the association of any topographic feature with external information. Each point object is classified with a particular feature code, such as post-box or bench-mark; likewise, a line feature could be labelled as a building outline or a public road edge. The feature-coding of polygons is the most difficult requirement of the DNF, as it requires the inferring of information that is not present in the Land-Line data. Properly classified area features greatly add to the intelligence of the resulting OS MasterMap data, allowing a myriad of valuable analyses to be carried out. The OS has accomplished high quality polygon classification semi-automatically, largely by examining the feature codes of the lines that bound each polygon. Using novel feature-coding techniques, the accuracy can be further improved.
The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian... more The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian co-ordinatebased point, line and text label features within the tile-based Land-Line® Database. Under their Digital National Framework™(DNF™) project, this data has been re-engineered into a topologically structured format known as OS MasterMap™[Ordnance Survey]. This required the modelling of the area features enclosed by the line data as polygon objects. This new polygon-enriched data can be provided seamlessly for pre- ...
We describe the application of analogical structure matching to the problem of classifying object... more We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of these attributes from the sample data. Our domain-specific Cartographic Structure Matching Algorithm is then introduced and explained. The fusion of our ...
We describe the application of analogical structure matching to the problem of classifying object... more We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of these attributes from the sample data. Our domain-specific Cartographic Structure Matching Algorithm is then introduced and explained. The fusion of our algorithm's results with other classification techniques is mentioned, and some examples of the detection of misclassified polygons are provided. We finally provide a preliminary evaluation of our classification technique and suggest some future developments.
Automatic categorization of large-scale topographic vector data into roads, buildings and similar... more Automatic categorization of large-scale topographic vector data into roads, buildings and similar classes typically examines each object description in isolation. We describe a cartographic structure matching (CSM) algorithm that automatically classifies objects in topographic maps by examining the context of the object. Matching clusters of objects against known templates serves to categorize ambiguous polygons by including context in the categorization process. We describe a number of applications that emerged from the core structure-matching algorithm, addressing problems of error detection, rejoining partitioned objects, composite object identification and data quality estimation.
The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian... more The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian co-ordinatebased point, line and text label features within the tile-based Land-Line® Database. Under their Digital National Framework™(DNF™) project, this data has been re-engineered into a topologically structured format known as OS MasterMap™[Ordnance Survey]. This required the modelling of the area features enclosed by the line data as polygon objects. This new polygon-enriched data can be provided seamlessly for pre- ...
Artificial Intelligence and Cognitive Science, 2001
We describe the application of analogical structure matching to the problem of classifying object... more We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of
The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian... more The Ordnance Survey has traditionally recorded the large-scale topography of Britain as Cartesian co-ordinatebased point, line and text label features within the tile-based Land-Line® Database. Under their Digital National Framework™(DNF™) project, this data has been re-engineered into a topologically structured format known as OS MasterMap™[Ordnance Survey]. This required the modelling of the area features enclosed by the line data as polygon objects. This new polygon-enriched data can be provided seamlessly for pre- ...
We describe the application of analogical structure matching to the problem of classifying object... more We describe the application of analogical structure matching to the problem of classifying objects in structured cartographic data. The reasons for and the requirements of such a classification are firstly outlined. The attributes on which the structural matching will operate and the representation of this data in Prolog are then described. A brief mention is made of the extraction of these attributes from the sample data. Our domain-specific Cartographic Structure Matching Algorithm is then introduced and explained. The fusion of our ...
Automatic categorization of large-scale topographic vector data into roads, buildings and similar... more Automatic categorization of large-scale topographic vector data into roads, buildings and similar classes typically examines each object description in isolation. We describe a Cartographic Structure Matching (CSM) algorithm that automatically classifies objects in topographic maps by examining the context of the object. Matching clusters of objects against known templates serves to categorize ambiguous polygons by including context in the categorization process. We describe a number of applications that emerged from the core structure-matching algorithm, addressing problems of error detection, rejoining partitioned objects, composite object identification and data quality estimation.
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Papers by Leo Mulhare