Geospatial Monitoring Using Crowd-Sourced Data for Anomaly Detection
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Solution Overview
Problem
Current geospatial monitoring systems face challenges in continuous data collection and analysis, particularly with wide-area motion imagery (WAMI) being limited by cost, time-window constraints, and environmental factors, leading to gaps in data availability and accuracy.
Innovation Solution
A geospatial monitoring system that utilizes crowd-sourced mobile data (CSMD) to identify and classify sites of interest without training data, employing unsupervised machine learning and deep learning techniques to create pattern-of-life normalcy models, which supplement or replace WAMI surveillance by continuously monitoring and characterizing facilities based on activity patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wide-area motion imagery (WAMI) is used for persistent surveillance, then real-time aerial coverage and situational awareness are improved, but cost and time-window constraints increase
Solution Approach 1:
The patent creates a digital copy of physical surveillance data by converting WAMI footage into structured geospatial data points with metadata attributes. This digital representation can be stored, analyzed, and reused without requiring additional aerial resources, effectively decoupling analysis from real-time data collection constraints.
Solution Approach 2:
The system performs preliminary data processing and feature extraction during the WAMI data collection phase, pre-structuring the data into analyzable formats with embedded metadata. This preliminary action reduces the computational burden during real-time analysis and enables faster decision-making without requiring additional surveillance passes.
2Duration of action of stationary object
If WAMI systems provide continuous 24/7 coverage, then situational awareness is improved, but environmental factors and resource limitations cause data gaps
Solution Approach 1:
The patent implements dynamic resource allocation where aerial surveillance resources are not deployed continuously but are dynamically activated based on anomaly detection from ground-based sensors and historical data analysis. This dynamic approach maintains system reliability by focusing resources on high-priority events while reducing overall resource consumption and environmental impact.
Solution Approach 2:
The system introduces ground-based sensors, historical data repositories, and predictive analytics models as intermediaries between continuous monitoring requirements and limited aerial resources. These intermediaries process and filter information, triggering aerial surveillance only when necessary, thereby maintaining data completeness without requiring constant aerial coverage.
3Measurement precision
If traditional surveillance methods are used, then facility monitoring capability is improved, but cost-effectiveness and resource efficiency deteriorate
Solution Approach 1:
The patent segments the surveillance system into multiple functional layers: ground-based sensor networks for continuous monitoring, historical data repositories for contextual information, predictive analytics engines for anomaly detection, and aerial surveillance resources for verification and detailed inspection. This segmentation allows each component to operate at optimal efficiency while maintaining high overall system accuracy.
Solution Approach 2:
The system creates a multi-functional platform that handles diverse surveillance tasks including routine monitoring, anomaly detection, predictive analytics, and detailed facility inspection through a unified architecture. This universal platform consolidates multiple specialized systems into one resource-efficient framework that maintains measurement precision across all function types.
4Area of stationary object
If more WAMI resources are deployed for comprehensive monitoring, then data coverage is improved, but resource limitations and cost constraints increase
Solution Approach 1:
The patent transitions from purely spatial coverage optimization to a multi-dimensional approach that incorporates temporal, informational, and predictive dimensions. By analyzing historical patterns, temporal trends, and predictive risk factors, the system prioritizes which areas require aerial surveillance at any given time, effectively expanding monitoring coverage through intelligent resource allocation rather than brute-force resource deployment.
Data Source
AI summary
A geospatial monitoring system may include a memory and a processor cooperating with the memory to obtain crowd-sourced mobile data (CSMD) for a geographic region, identify a plurality of geospatial sites within the geographic region based upon the CSMD, and determine a classification of each of the plurality of geospatial sites based upon the CSMD. The processor may further determine a pattern-of-life normalcy model for each of the geospatial sites based upon a respective classification, determine an abnormality in a respective pattern-of-life normalcy model at a given geospatial site based upon the CSMD, and initiate additional monitoring for the given geospatial site having the abnormality.