Cognitive Image Analysis for Event Prediction with Lower Network Traffic
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Solution Overview
Problem
Existing property inspection systems for disaster risk assessment rely heavily on qualitative assessments, leading to inaccurate results and high network traffic due to the use of large aerial image data, which can be outdated and inefficient to recapture, resulting in insufficient network bandwidth and inaccurate damage estimation.
Innovation Solution
A system utilizing machine learning-based image analytics that processes local data first to determine image recency, extracts attributes and distances within images, and generates event predictions, reducing the need for remote data queries and optimizing network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If aerial image data is used for property inspection, then damage estimation accuracy is improved, but network traffic increases and bandwidth is insufficient
Solution Approach 1:
The patent extracts only the essential visual features and attributes from aerial images using computer vision algorithms, rather than transmitting or processing the complete high-resolution image data. This extraction process isolates the critical information needed for damage assessment while eliminating redundant data, thereby reducing network traffic while preserving estimation accuracy.
Solution Approach 2:
The system applies local quality by processing and analyzing specific regions of interest within aerial images rather than the entire image dataset. By focusing computational resources on areas showing potential damage or relevance to the assessment, the system reduces overall data processing requirements and network bandwidth consumption while maintaining accurate damage estimation.
2Device complexity
If qualitative assessment by inspector is used, then device complexity is reduced, but measurement precision of disaster risk assessment deteriorates
Solution Approach 1:
The system implements self-service by enabling automated image analysis and disaster risk assessment through computer vision algorithms. The system processes aerial images, extracts relevant features, and generates assessments autonomously without requiring manual inspection, thereby eliminating the trade-off between system complexity and assessment accuracy.
Solution Approach 2:
The patent replaces the mechanical inspection process (manual qualitative assessment by inspectors) with an automated computational system. By substituting human inspection with algorithm-based image analysis, the system achieves consistent, objective, and precise disaster risk assessments while managing complexity through software automation.
3Measurement precision
If aerial images are recaptured to ensure data recency, then measurement precision is improved, but productivity decreases and time consumption increases
Solution Approach 1:
The system applies preliminary action by implementing automated recency detection and validation mechanisms that assess whether existing aerial image data is sufficiently current without requiring full recapture. This preliminary assessment prevents unnecessary image collection activities, maintaining data recency accuracy while preserving inspection productivity.
Solution Approach 2:
The system implements dynamics by adaptively determining when aerial image recapture is necessary based on detected changes, data age thresholds, and risk assessment requirements. Rather than following a fixed recapture schedule, the system dynamically adjusts data collection activities, ensuring data recency while optimizing inspection efficiency and reducing redundant operations.
Data Source
AI summary
Systems and methods for reducing network traffic associated with generating event predictions based on cognitive image analysis are disclosed. The system receives a user input indicating a location identifier of a location of interest. The system queries a data store to determine whether an image associated with the location of interest satisfies a data recency criterion. In response to the image failing to satisfy the data recency criterion, the system queries a remote data store to determine whether another, additional image associated with the location of interest satisfies the data recency criterion. In response to the additional image satisfying the data recency criterion, the system extracts one or more image-based attributes of the additional image and determines a distance between objects of the image. The system generates an event prediction based on the image-based attributes, the distance, and location-based attributes using a machine learning model to be displayed.