Automated Digital Elevation Model Generation via Resolution-Based Data Merging

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Solution Overview

Problem

Existing systems for generating elevation models from multiple sets of elevation measurements require extensive expert input and involve multiple iterations with manual analysis, making them inefficient and time-consuming.

Innovation Solution

A computer-based system that merges multiple sets of elevation data points using a merging strategy based on resolution compatibility, generating a combined set of elevation data points and creating an elevation model through multiple estimation processes, eliminating the need for manual expert feedback and reducing processor cycles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If multiple sets of elevation measurements from different organizations are manually merged and analyzed, then measurement precision and reliability are improved, but loss of time and productivity deteriorate significantly

Engineering Contradiction:

Improveelevation model accuracyVSAvoidmodel generation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs automatic merging of elevation data sets using computational algorithms that independently determine resolution compatibility, select appropriate merging methods (grid-cell based or hull-based), and generate elevation models without requiring expert intervention or manual analysis at each iteration step

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-processes elevation data sets by determining their resolutions and compatibility characteristics before the actual merging process, allowing for optimized selection of merging strategies and reducing iterative refinement time

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If multiple iterations with manual expert analysis are performed, then manufacturing precision of the elevation model is improved, but device complexity and ease of operation worsen

Engineering Contradiction:

Improveelevation model accuracyVSAvoidprocess complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system replaces manual expert analysis and iterative refinement processes with automated computational algorithms that systematically evaluate resolution compatibility, select merging methods, and generate elevation models through programmed decision-making rather than human expert judgment

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the state of the merging process from manual iterative adjustment to automated parameter-driven processing, where resolution values and compatibility thresholds guide the selection of merging strategies and control the generation process

Inventive Principle:

Principle #35Parameter changes

3Reliability

If extensive manual analysis and expert feedback are used, then reliability of the elevation model is improved, but productivity and ease of operation deteriorate

Engineering Contradiction:

Improveelevation model reliabilityVSAvoidmodel generation throughput

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements automated feedback mechanisms where the computational algorithms evaluate the compatibility of elevation data sets, assess resolution matching, and adjust merging strategies based on quantitative criteria rather than requiring external expert feedback loops

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs self-validation and quality assessment through automated algorithms that verify resolution compatibility and merging appropriateness, eliminating the need for external expert review while maintaining reliability through systematic computational evaluation

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10339707B2Automated generation of digital elevation models

Publication Date: 2019.07.02 OCEAN NETWORKS CANADA SOC

AI summary

Digital elevation models are generated based on multiple sets of elevation measurements. For example, multiple sets of elevation measurements are merged to create a combined set of elevation measurements by using different merging methods, based on the resolutions of the sets within a given physical landscape. An elevation model can then be generated based on the combined set of elevation measurements by using multiple estimation processes in combination to generate estimated elevations and uncertainty values for various areas of the given physical landscape.