Aerial Image Selection for Energy Infrastructure Feature Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying and tracking energy infrastructure features, such as oilfield sites and solar power stations, are manual, time-consuming, and costly, leading to outdated and inaccurate data, and lack efficient water management solutions for hydraulic fracturing operations.
Innovation Solution
A system and method for processing aerial images using an EI feature recognition model to automatically identify and classify energy infrastructure features and their status, integrating with supplemental information sources for enhanced accuracy and efficiency, and providing timely information on water sourcing and disposal options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify and track energy infrastructure features, then data accuracy may be maintained through human review, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical review processes with automated image processing systems using machine learning models. The system automatically analyzes aerial and satellite images to identify energy infrastructure features, substituting human manual inspection with computational algorithms that process images rapidly while maintaining identification accuracy.
Solution Approach 2:
The system creates digital copies of aerial and satellite imagery and processes these copies through automated algorithms. By working with image copies rather than requiring manual field surveys or direct observation, the system achieves rapid analysis without sacrificing the ability to accurately identify infrastructure features.
2Reliability
If comprehensive aerial image processing is performed on all available images, then complete identification of energy infrastructure features is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The system extracts and processes only the most relevant aerial and satellite images for identification purposes. By selecting specific images that contain energy infrastructure features rather than processing all available imagery, the system maintains complete identification capability while reducing overall processing time and computational resource requirements.
Solution Approach 2:
The patent segments the image processing task into distinct stages: initial image selection, automated processing of selected images, and manual review only when necessary. This segmentation allows the system to achieve complete identification through automated methods for most cases while reserving manual review for ambiguous situations, thereby improving overall processing efficiency.
3Ease of operation
If traditional water management methods are used in hydraulic fracturing operations, then operational simplicity is maintained, but water transportation costs increase and profit margins decrease
Solution Approach 1:
The system provides feedback to operators about nearby water sources and disposal locations based on geographic analysis. This feedback enables operators to make informed decisions about water management strategies, allowing them to maintain operational simplicity while reducing transportation costs by selecting locally optimal water sources and disposal sites.
Solution Approach 2:
The system enables water management through automated identification and analysis, allowing the operation to self-determine optimal water sources and disposal locations without requiring complex external management systems. The automated processing of aerial and satellite images provides the information needed for cost-effective water management while maintaining operational simplicity.
Data Source
AI summary
A computer-implemented method for selecting aerial images for image processing to identify Energy Infrastructure (EI) features is provided. The method includes performing image processing on aerial images of a portion of global terrain captured at different times to determine differences in terrain content the captured images. Aerial images are selected for further image processing according to identified differences in terrain content. The selected images are imaged processed via an EI feature recognition type to identify EI features within the images.