Cell-Site Image Validation for E911 Location Accuracy
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Solution Overview
Problem
Existing E911 location-based services face inaccuracies in locating user devices due to varying and inaccurate location details of cell sites in GMLC network nodes and wireless carrier databases, leading to incorrect routing of emergency calls.
Innovation Solution
Implementing artificial intelligence (AI) and machine learning (ML) automation to validate latitude/longitude data using satellite images of cell sites, generating models to accurately determine the presence or absence of cell sites, thereby correcting location errors in GMLC configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of cell site location data is performed, then location accuracy can be validated, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical verification processes with automated electronic systems. Specifically, it uses satellite imagery automated analysis, machine learning models, and computer vision algorithms to detect and validate cell site locations, replacing the need for manual inspection of maps and satellite images by human operators.
Solution Approach 2:
The system enables self-service by allowing the automated verification process to validate cell site location data independently without human intervention. The machine learning models automatically compare database locations with satellite imagery, detect discrepancies, and flag errors for correction, making the verification process autonomous.
2Reliability
If comprehensive validation of cell site location data is performed, then location error rate decreases, but system complexity increases
Solution Approach 1:
The patent segments the validation process into distinct functional modules: satellite imagery acquisition, image processing, machine learning inference, discrepancy detection, and error flagging. Each module handles a specific aspect of validation, making the overall complex system manageable through modular design and independent optimization.
Solution Approach 2:
The patent introduces machine learning models and automated image analysis systems as intermediaries between the raw satellite imagery and the final validation decisions. These intermediaries process and interpret the complex imagery data, translating it into actionable validation results without requiring direct human analysis of the complex visual data.
3Productivity
If automated AI/ML processes are implemented to validate cell site locations, then productivity increases, but implementation cost and technical complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models with labeled satellite imagery data before deployment. The system performs preliminary validation checks and establishes baseline accuracy metrics before full-scale operation, ensuring the automated processes are ready and validated before processing production data at high volume.
Solution Approach 2:
The patent incorporates feedback mechanisms where validation results are continuously fed back into the system to improve accuracy. Discrepancies detected by the automated system are flagged and can be used to retrain machine learning models, creating a continuous improvement loop that enhances validation accuracy over time while maintaining automated high-throughput operation.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining a list of a plurality of known cell sites, the list comprising for each known cell site of the plurality of known cell sites a respective set of known geospatial coordinates; obtaining for each set of known geospatial coordinates, a corresponding aerial image; applying each corresponding aerial image to an automated process to generate a model, the model being usable to determine whether a particular test aerial image depicts a cell site; obtaining from a database an identification of an asserted cell site, the identification of the asserted cell site comprising a set of asserted geospatial coordinates; obtaining for the set of asserted geospatial coordinates, a test aerial image; applying the test aerial image to the model to determine whether the test aerial image depicts the asserted cell site, resulting in a determination; and outputting the determination of whether the test aerial image depicts the asserted cell site. Other embodiments are disclosed.