Hierarchical Tile-Based Digital Elevation Model Generation
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Solution Overview
Problem
Existing map data generation technologies face challenges in providing high-quality digital elevation models (DEMs) that can scale to different resolutions required by various services and applications, such as autonomous driving and weather modeling, due to limitations in integrating and updating elevation data from different sources efficiently.
Innovation Solution
A hierarchical tile-based digital elevation model system is developed, which processes map elevation data to create a hierarchical resolution tile representation with multiple levels of resolution, allowing for efficient interpolation and alignment of elevation data points, and integrates data from different sources to provide flexible and quick retrieval of elevation data at appropriate resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single high-resolution digital elevation model is generated to meet the most demanding applications, then the highest measurement precision is achieved, but the data processing time and resource consumption increase significantly
Solution Approach 1:
The patent divides the digital elevation model into a hierarchical structure with multiple resolution levels (coarse to fine). Each level represents the same geographic area but at different detail levels. This segmentation allows the system to provide appropriate resolution based on application needs, avoiding unnecessary processing of high-resolution data when lower resolution suffices, thus reducing processing time while maintaining required precision.
Solution Approach 2:
The system dynamically selects and processes only the necessary resolution level based on the specific application requirements and query parameters. Instead of always generating and processing the highest resolution data, the system adapts the processing intensity to match the actual needs, optimizing the balance between precision and processing time.
2Manufacturing precision
If high-resolution elevation data is processed and stored for all areas, then the manufacturing precision of map data is improved, but the storage space and computational resources required increase
Solution Approach 1:
The patent implements a multi-resolution hierarchical structure where the geographic area is divided into tiles at different resolution levels. Coarse-resolution tiles cover larger areas with less detail, while fine-resolution tiles cover smaller areas with higher detail. This segmentation reduces the total data volume by storing only the necessary level of detail for each region, while still providing access to high-resolution data where needed.
Solution Approach 2:
Different regions of the map data are stored at different resolution levels based on local requirements. Areas requiring high precision (such as urban regions or areas of interest) are stored at finer resolutions, while less critical areas use coarser resolutions. This local quality approach optimizes storage efficiency while maintaining manufacturing precision where it matters most.
3Adaptability or versatility
If multiple resolution levels are integrated from different data sources, then the adaptability of the system to different applications is improved, but the device complexity increases
Solution Approach 1:
The patent segments the data integration process into distinct resolution levels, with each level handling data at its appropriate scale. This modular segmentation simplifies the integration complexity by allowing independent processing and validation at each resolution level, rather than attempting to integrate all data sources simultaneously at a single resolution.
Solution Approach 2:
The patent adds a resolution level dimension to the data structure, organizing elevation data along an additional axis (resolution/coarseness) beyond the traditional spatial dimensions. This dimensional organization provides a systematic framework for integrating multiple data sources, allowing the system to navigate between different resolution levels and data sources in a structured manner, thereby managing integration complexity.
Data Source
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AI summary
An approach is provided for providing a digital elevation model. For example, the approach involves processing, by a processor, map elevation data to create a hierarchical resolution tile representation of the digital elevation model for a geographic area. The hierarchical resolution tile representation includes a plurality of resolution levels, wherein each of the plurality of levels represents the digital elevation model at a different resolution. The digital elevation model includes a plurality of control points, wherein each of the plurality of control points is associated with an elevation data point determined from the map elevation data.