Geospatial Modeling Using 3D Cost Cube and Segmentation

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Solution Overview

Problem

Conventional geospatial modeling systems face challenges in generating high-resolution digital elevation models (DEMs) that require large data volumes, are computationally burdensome, and often result in blurred details and interpolation errors due to the use of larger correlation patches and ad-hoc data combination methods.

Innovation Solution

A geospatial modeling system that uses a processor to generate a 3D cost cube based on stereo-geographic image data and geographic feature data, adjusting cost coefficients to create a tiled triangulated irregular network (T-TIN) or raster grid model, allowing for smaller correlation patches and multiple stereo pairs for improved resolution and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If high-resolution DEMs are generated with increased data point density (≤1m spacing), then measurement precision and detail representation are improved, but the volume of data generated increases significantly, making processing extremely burdensome

Engineering Contradiction:

Improveelevation measurement precisionVSAvoiddata volume

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The image data is divided into smaller patches (e.g., 3×3 patches) for processing. This segmentation allows the system to handle high-resolution data in manageable units, reducing the computational burden while maintaining measurement precision at ≤1m spacing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent uses relatively small correlation patches (partial action) rather than processing entire images. This partial processing approach reduces data volume requirements while still achieving high-resolution DEM generation through strategic sampling and correlation of smaller regions

Inventive Principle:

Principle #16Partial or excessive action

2Device complexity

If larger correlation patches are used for data combination, then processing complexity is reduced, but the result becomes blurred and loses fine details

Engineering Contradiction:

Improveprocessing complexityVSAvoiddetail preservation

Core Design Contradiction:

Device complexityVSManufacturing precision

Solution Approach 1:

By segmenting the correlation process into smaller patches, the system maintains fine details that would otherwise be blurred in larger patches, while keeping processing complexity manageable through the structured approach of handling multiple small regions

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies different processing characteristics to different local regions through the patch-based approach, allowing fine details to be preserved in areas where they exist while maintaining overall processing efficiency through the localized nature of patch correlations

Inventive Principle:

Principle #3Local quality

3Device complexity

If ad-hoc techniques are used to combine multiple data sources, then system complexity is reduced, but interpolation errors increase and accuracy decreases

Engineering Contradiction:

Improvesystem complexityVSAvoidelevation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent merges multiple data sources (stereo image pairs, edge data, area correlation data, ground truth information) into a unified cost cube framework. This systematic combination reduces interpolation errors by integrating all available information consistently, improving elevation accuracy while maintaining manageable system complexity through the standardized cost cube approach

Inventive Principle:

Principle #5Merging (Combining)

4Measurement precision

If multiple stereo image pairs and multiple data sources are integrated, then measurement precision and detail representation are improved, but device complexity and processing requirements increase

Engineering Contradiction:

Improvegeospatial model accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The cost cube serves as a universal data structure that can accommodate multiple types of input data (stereo pairs, edge data, correlation data, ground truth). This multi-functional framework allows integration of diverse data sources without proportionally increasing system complexity, as the same cost cube structure handles all input types

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The cost cube acts as an intermediary structure that systematically integrates multiple data sources. By using this intermediate representation, the patent manages the complexity of combining multiple stereo pairs and data types while improving measurement precision through comprehensive data integration

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS7983474B2Geospatial modeling system and related method using multiple sources of geographic information

Publication Date: 2011.07.19 HARRIS CORP

AI summary

A geospatial modeling system may include at least one geospatial information database to store stereo-geographic image data and geographic feature data. A processor may cooperate with the geospatial information database for generating cost coefficients defining a three-dimensional (3D) cost cube using image matching operators based upon the stereo-geographic image data, adjusting the cost coefficients of the 3D cost cube based upon the geographic feature data to generate an adjusted 3D cost cube, and generating a geospatial model based upon solving the adjusted 3D cost cube, e.g. for a best cost surface. The system and method provide an integrated approach to creating a geospatial model using available data from multiple sources.