Topographical Map Data Generalisation Using Graph Theory
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Solution Overview
Problem
Existing systems for generalizing topographical map data, particularly for road networks, are computationally intensive and non-scalable, failing to efficiently handle complex intersections and requiring significant processing power, which is inefficient for dynamic zooming and updating of map data.
Innovation Solution
The use of graph theory to create a tracked network of stroke type objects representing single carriageway portions of dual carriageways, pairing them to collapse into centre lines, and connecting these centre lines to the road network, with the process optimized by inserting connection edges based on proximity and topographic nature, allowing for scalable map layers at different zoom levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional generalisation methods are used to collapse dual carriageways, then the road network can be represented at smaller scales, but the processing becomes computationally intensive and non-scalable
Solution Approach 1:
The patent segments the dual carriageway representation into multiple stroke objects, each representing a portion of the carriageway. These segmented strokes are then paired and collapsed efficiently using graph theory, avoiding the computational burden of processing the entire dual carriageway as a single complex object. This segmentation enables scalable processing of large map datasets.
Solution Approach 2:
The patent introduces centre lines as intermediary elements that represent collapsed dual carriageways. These centre lines serve as mediators between the detailed stroke objects and the simplified map representation, enabling efficient generalisation while preserving topological relationships. The centre lines are created by pairing strokes and collapsing them along their midlines.
2Measurement precision
If detailed topographical data is retained at all zoom levels, then map fidelity is maintained, but the data becomes unreadable at larger scales due to feature concentration
Solution Approach 1:
The patent implements dynamic generalisation where the level of detail in road network representation adapts automatically based on the map scale. At larger scales, dual carriageways are collapsed to centre lines; at smaller scales, the full detailed representation is restored. This dynamic adjustment maintains both fidelity and readability across different zoom levels.
Solution Approach 2:
The patent applies different levels of generalisation to different parts of the map based on local requirements. Individual dual carriageway sections are selectively collapsed or retained depending on the local scale and context, rather than applying uniform generalisation across the entire map. This preserves important local details while achieving overall readability.
3Reliability
If manual updates are performed on multiple zoom versions of the map, then data consistency is maintained, but the time required for updates becomes very time consuming
Solution Approach 1:
The patent performs preliminary generalisation processing on the detailed topographical data to create a hierarchical structure of map representations. This preliminary processing organizes the data so that updates can be propagated efficiently through the hierarchy, avoiding the need to manually update each zoom level separately. The graph-based structure enables automatic update propagation.
Solution Approach 2:
The patent creates a universal data structure using graph theory that serves multiple functions: it represents the detailed road network, enables automatic generalisation to multiple zoom levels, and facilitates efficient update propagation. This multi-functional structure eliminates the need for separate manual update processes for each zoom level while maintaining data consistency.
Data Source
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AI summary
Embodiments of the present disclosure aim to improve upon systems and methods for generalising topographical map data relating to the road network for use at different scale levels. The existing systems of the prior art are able to collapse the two carriageways of a dual carriageway to a single centreline representing the dual carriageway for many of the roads of a national road network by tracking along one way sections of carriageway and generating pairs of carriageways for collapse, but fall short of being able to compute complicated intersections. Further, existing methods place heavy demands on processing power and are not suitable for change only refreshes. Embodiments of the present invention uses graph theory to create a tracked network of stroke type objects representing the length of single carriageway sections of dual carriageways, and then successfully pairs them such that they can be collapsed to a centre line and connected to the road network. The process reduces computation time, thereby speeding the rendering process for map imagery.