Geospatial Entity Resolution and Trajectory Prediction With Missing Data
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Solution Overview
Problem
Traditional geospatial analysis systems face challenges in handling missing data, managing entity relationships, and predicting complex trajectories of moving entities due to reliance on simple extrapolation techniques and difficulty in integrating diverse data types.
Innovation Solution
A geospatial moving entity analysis system utilizing AI models, such as transformers, for spatiotemporal predictions, incorporating spherical geometry, and implementing features like trajectory prediction, missing value imputation, entity resolution, and multi-modal data fusion to enhance geospatial understanding and prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple extrapolation techniques are used for trajectory prediction, then the system complexity is reduced, but the prediction accuracy for complex trajectories deteriorates
Solution Approach 1:
The patent replaces simple mechanical extrapolation techniques with AI-based prediction models (transformers, GNNs, LSTMs) that can capture complex non-linear patterns in trajectory data, achieving higher prediction accuracy without manual complexity management
Solution Approach 2:
The system dynamically adjusts prediction parameters based on data characteristics, switching between different AI models or adjusting model complexity adaptively to balance between computational resources and prediction accuracy for different trajectory types
2Ease of operation
If traditional geospatial analysis methods are used, then the ease of operation is maintained, but the ability to handle missing data and diverse data types deteriorates
Solution Approach 1:
The patent implements a universal AI-based processing framework that can handle multiple data types (spatial, temporal, attribute data) and various data quality scenarios (complete, partial, missing data) through a single integrated system, eliminating the need for separate specialized processing pipelines
Solution Approach 2:
The system automatically detects data quality issues and selects appropriate imputation strategies or processing approaches without manual intervention, allowing the system to self-adjust to different data scenarios while maintaining ease of operation through automated decision-making
3Measurement precision
If AI models are introduced for spatiotemporal predictions, then the prediction accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent divides the prediction system into separate modular AI models (transformers for sequence prediction, GNNs for spatial relationships, LSTMs for temporal patterns) that can be independently selected and combined based on specific prediction tasks, allowing complexity to be segmented and managed rather than monolithic
Solution Approach 2:
The system applies AI models selectively only where needed based on data characteristics and prediction requirements, using simpler methods for straightforward cases and reserving complex AI models for challenging predictions, thus avoiding unnecessary complexity while maintaining high accuracy when required
Data Source
AI summary
Various example embodiments provide for systems, methods, techniques, instruction sequences, and devices for geospatial analysis of one or more moving entities (or moving objects). In particular, various embodiments provide for entity resolution with respect to one or more records, where entity resolution can comprise identifying and linking multiple records (e.g., entity records) that refer to a same real-world entity (e.g., moving entity). According to various example embodiments, the entity resolution comprises resolving data discrepancies, such as different spellings of names, reporting errors, or the use of various identifiers for the same entity.