Aerial Image Processing for Energy Infrastructure Feature Identification
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Solution Overview
Problem
Current methods for identifying energy infrastructure features and status in large geographical areas are manual, time-consuming, costly, and often result in outdated data, especially in the oil and gas industry, where features like frac-water pits and drilling activities require timely and accurate information for efficient water management and resource allocation.
Innovation Solution
A system utilizing image processing and AI-based EI feature recognition models to automatically identify and classify energy infrastructure features from aerial images, combining image data with supplemental information to enhance accuracy and reduce costs, enabling timely and reliable updates on feature types and status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify energy infrastructure features, then data can be obtained, but the process is time-consuming and results in outdated data
Solution Approach 1:
The patent replaces manual mechanical identification methods with an automated image processing system that uses aerial imagery and machine learning algorithms to detect and classify energy infrastructure features, eliminating human labor while improving both speed and accuracy
Solution Approach 2:
The system creates automated digital copies of infrastructure features through aerial image analysis, generating detailed records of feature locations, types, and statuses without requiring physical site visits or manual data collection
2Reliability
If manual identification methods are used, then features can be detected, but the process is costly
Solution Approach 1:
The system enables self-service automated identification where the image processing algorithm independently detects, classifies, and updates infrastructure feature data without requiring human intervention, reducing operational costs while maintaining high reliability through consistent automated processing
Solution Approach 2:
The patent employs advanced machine learning models and sophisticated image processing techniques that accelerate the identification process, enabling rapid and reliable detection of infrastructure features at lower costs compared to traditional manual methods
3Productivity
If automated image processing is applied, then identification efficiency is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex image processing task into distinct segments: aerial image acquisition, feature detection, classification, and data updating. This modular segmentation improves processing efficiency while managing system complexity through organized, independent functional blocks
Solution Approach 2:
The image processing system is designed with multi-functionality to handle various types of energy infrastructure features (wells, pipelines, storage facilities) using a single unified platform, improving productivity across different applications while avoiding the complexity of multiple specialized systems
Data Source
AI summary
A computer-implemented method for processing images to identify Energy Infrastructure (EI) features within aerial images of global terrain is provided. The image processing method identifies information about EI features by applying an EI feature recognition model to aerial images of global terrain. The EI feature recognition model identifies the EI feature information according to image content of the aerial image. The method further provides updates to the identification of the EI feature information according to relationships between identified EI features.