Common-Change-Agnostic Model for Rare Geographic Change Detection
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Solution Overview
Problem
Current methods face challenges in detecting rare changes in geographic regions, such as those caused by disasters or remodeling, while being agnostic to common changes like shadows or tree occlusions, and often struggle with sparse data and biased insurance claim data.
Innovation Solution
A system and method that trains a common-change-agnostic model to output consistent representations for common changes, allowing for the detection of rare changes by differing outputs, and uses self-supervised learning to overcome data sparsity, leveraging visual features for change detection and storing representations instead of measurements to reduce data storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current change detection methods are used, then common changes can be detected, but rare changes are missed and false positives increase due to inability to distinguish from common changes
Solution Approach 1:
The patent segments change detection into two distinct pathways: common change detection and rare change detection. By dividing the detection space and applying different methodologies to each segment, the system achieves higher precision for rare changes while maintaining reliability through specialized processing for each change type
Solution Approach 2:
The patent introduces an intermediary representation model that transforms raw geographic region measurements into a standardized feature space. This intermediary representation enables the system to distinguish between common and rare changes more effectively, improving both measurement precision and detection reliability by serving as a bridge between raw data and detection algorithms
2Loss of information
If detailed measurements are stored for all geographic regions, then comprehensive analysis is possible, but data storage requirements increase significantly
Solution Approach 1:
The patent creates compressed representations (copies) of geographic region measurements through the representation model. Instead of storing all raw measurement data, the system stores condensed feature vectors that capture essential information while occupying minimal storage space, thus reducing data storage volume while preserving information completeness
Solution Approach 2:
The patent transforms detailed measurement data into a different parameter space through the representation model. By changing from storing raw measurements to storing extracted features, the system achieves efficient compression that maintains analytical capability while significantly reducing storage requirements
3Measurement precision
If supervised learning is used for change detection, then accuracy can be high, but data sparsity and bias in insurance claim data limit model training
Solution Approach 1:
The patent implements self-supervised learning where the representation model learns to extract meaningful features without requiring labeled training data. The model serves itself by learning from the structure and patterns inherent in the measurement data, enabling accurate change detection even when insurance claim data is sparse or biased, thus maintaining both precision and training reliability
Data Source
AI summary
In variants, the method for change analysis can include: training a representation model and evaluating a geographic region. In an example, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.