ML-Based Facilities Infrastructure Data Inference and Mapping
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Solution Overview
Problem
Existing facilities infrastructure data is often unreliable, outdated, or non-existent, leading to challenges in call before you dig programs due to inconsistent and inaccurate information about facility locations and infrastructure, which hinders efficient planning and maintenance.
Innovation Solution
A system utilizing machine-learning models to ingest and process facilities infrastructure data, inferring missing features and providing scores for facilities, generating maps, and offering visualization interfaces to users, thereby improving data accuracy and consistency through user contributions and automated tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional on-site survey methods are used for facilities infrastructure data collection, then data reliability is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces traditional mechanical on-site survey methods with automated electronic data collection systems. Machine learning models process satellite imagery, aerial photography, and other remote sensing data to automatically extract facilities infrastructure information, eliminating the need for manual field surveys while maintaining high data reliability through algorithmic accuracy and consistency.
Solution Approach 2:
The system enables facilities infrastructure data collection to be self-service through automated processing. The machine learning models independently analyze raw data from multiple sources, perform feature extraction, and generate structured facility information without requiring human intervention at each step, thereby reducing both time consumption and operational complexity while preserving data reliability.
2Reliability
If traditional on-site survey methods are used for facilities infrastructure data collection, then data reliability is improved, but operational complexity increases significantly
Solution Approach 1:
The patent merges multiple data collection and processing functions into a single integrated machine learning system. The model simultaneously handles data ingestion from multiple sources, feature extraction, facility identification, and data validation in one unified process, reducing operational complexity while maintaining data reliability through consistent algorithmic processing across all functions.
Solution Approach 2:
The system replaces complex manual survey operations with automated machine learning processing. The AI models handle the complexity of data analysis, pattern recognition, and facility identification automatically, transforming a multi-step manual process into a streamlined automated workflow that reduces operational complexity while preserving data reliability.
3Measurement precision
If machine-learning models are used to process facilities infrastructure data, then data accuracy is improved through feature inference, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive facilities infrastructure datasets before deployment. The models learn to infer missing features and predict facility characteristics in advance, enabling accurate data completion and enhancement when processing actual facilities data, thereby improving measurement precision while managing computational complexity through prior preparation.
Solution Approach 2:
The system uses copying by creating virtual representations of facilities infrastructure through machine learning-generated data. The models infer missing features by copying patterns and relationships learned from training data, allowing accurate reconstruction of facility information without requiring complete original data, thus improving data accuracy while controlling computational requirements through pattern-based inference.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a device with a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of ingesting facilities infrastructure data for more than one facility; training a machine-learning (ML) model from the facilities infrastructure data, wherein the ML model infers features absent in the facilities infrastructure data and yields a score of a facility described by the facilities infrastructure data; receiving a query for a region from a user; identifying one or more facilities provided in the region; generating a map of the one or more facilities using the ML model; and providing a visualization interface to the user including the map and the score of the one or more facilities responsive to the query. Other embodiments are disclosed.