Automated Physical Object Classification for Telecom Site Selection
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Solution Overview
Problem
Network densification faces challenges in identifying suitable locations for installing telecommunications equipment due to varying local regulations and the time-consuming, costly process of manual site inspections, which are prone to human error.
Innovation Solution
A system utilizing machine learning and intelligent object recognition frameworks to classify physical objects, such as utility poles, by extracting attributes from images and determining eligibility for telecommunications equipment installation based on local ordinances, thereby automating the site selection process and reducing manual labor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to identify cell sites, then compliance with local regulations can be verified, but the process is time consuming and costly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system that uses machine learning algorithms to analyze photographs of potential cell sites. The system automatically extracts features from images and determines compliance with local regulations, eliminating the need for engineers to physically visit each site while maintaining high accuracy in compliance verification.
Solution Approach 2:
The patent creates digital copies (images) of physical cell sites and performs compliance verification on these copies rather than requiring physical inspection. The image processing system analyzes photographs to extract relevant features and determine regulatory compliance, allowing multiple sites to be evaluated simultaneously without additional travel time or manual effort.
2Measurement precision
If engineers manually inspect candidate cell sites, then detailed property assessment can be performed, but the process is costly and prone to human error
Solution Approach 1:
The patent replaces human engineers with an automated computer vision system that uses machine learning models trained on labeled data to assess cell site properties. The system consistently applies the same evaluation criteria to all sites, eliminating human error and variability while reducing costs through automation. The trained models can identify relevant features and make compliance determinations without human intervention.
Solution Approach 2:
The patent performs preliminary training of machine learning models using labeled data from previously inspected sites. This preliminary action creates a knowledge base that the system can apply to new sites, ensuring consistent and accurate assessment without requiring engineers to re-evaluate criteria for each new inspection. The pre-trained models enable rapid, accurate assessment of candidate sites.
3Productivity
If the number of cell sites is increased for network densification, then available network capacity increases, but identifying compliant sites becomes more difficult due to varying local regulations
Solution Approach 1:
The patent creates a universal image processing system that can handle multiple types of local regulations and site types through a single platform. The machine learning models are trained to recognize various features relevant to different regulatory requirements, allowing the system to evaluate diverse cell site candidates across different jurisdictions without requiring separate manual assessment processes for each regulation type.
Solution Approach 2:
The patent uses digital image copies to represent physical sites, allowing the system to evaluate numerous candidate sites simultaneously by processing images in parallel. This approach simplifies the identification process by converting physical inspection complexity into automated image analysis, where multiple sites can be assessed through the same standardized image processing pipeline regardless of local regulation variations.
Data Source
AI summary
In one embodiment, a method includes receiving, by one or more interfaces, a location of a physical object and receiving, by the one or more interfaces and from a data platform, an image associated with the physical object and the location of the physical object. The method also includes extracting, by one or more processors and from the image, an attribute associated with a feature of the physical object and classifying, by one or more processors, the attribute, wherein classifying the attribute comprises associating the attribute with a characteristic of the feature of the physical object. The method further includes classifying, by the one or more processors, the physical object and determining, by the one or more processors, to identify the physical object as eligible for modification.