Automated Gas Emission Parameter Estimation from Overhead Spectral Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for monitoring gas emissions lack accuracy and specificity, particularly in regions with limited instrumentation, leading to low spatial and temporal resolutions and limited generalization to new sources, necessitating improved systems for precise, frequent, and automated measurements across large regions.
Innovation Solution
A method and system utilizing overhead sensors and deep-learning classification models to analyze spectral signals from geospatial areas, trained with historical data and iterative optimization, to determine gas emission parameters, including reflectance, radiance, and concentration, with support from auxiliary data like topography and weather data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gas emission monitoring methods are used, then deployment is simpler, but measurement precision and spatial resolution are insufficient
Solution Approach 1:
The patent introduces deep-learning classification models as an intermediary between spectral signals and gas emission parameters. These models process complex spectral data and transform it into accurate gas emission estimates, resolving the contradiction by adding an intelligent processing layer that improves measurement precision without requiring direct complex instrumentation at the measurement point
Solution Approach 2:
The patent replaces traditional mechanical/sensor-based gas detection systems with a remote sensing approach using spectral signals and deep-learning models. This substitution allows for high-precision gas emission monitoring over large areas without deploying complex instrumentation infrastructure, thereby improving measurement precision while maintaining deployment simplicity
2Measurement precision
If existing detection methods are used, then implementation is easier, but spatial and temporal resolution are low
Solution Approach 1:
The patent extracts and processes spectral signals from satellite or aerial sensors to derive gas emission parameters. By taking out the spectral signal extraction and classification functions and processing them through deep-learning models, the system achieves high spatial resolution over large areas without requiring complex local instrumentation infrastructure
Solution Approach 2:
The patent transitions from traditional point-based or area-averaged gas measurement to pixel-level or sub-pixel-level spatial resolution by processing spectral signals in the spectral dimension through deep-learning classification. This dimensional transformation enables high spatial resolution monitoring over extensive geographic areas simultaneously
3Area of stationary object
If automated monitoring systems are deployed over large regions, then measurement coverage increases, but data processing complexity increases
Solution Approach 1:
The deep-learning classification models serve as an intermediary that processes vast amounts of spectral signal data from large monitoring areas and transforms it into manageable gas emission parameter estimates. This intelligent processing layer handles the complexity of large-scale data processing automatically, enabling extensive monitoring coverage without proportionally increasing operational complexity
Solution Approach 2:
The system performs self-service through automated deep-learning-based processing that handles data processing complexity internally. The models automatically learn patterns from spectral signals and generate gas emission estimates without requiring manual intervention or complex processing workflows, thereby enabling large-area monitoring while keeping operational complexity manageable
4Measurement precision
If spectral signals from multiple bands and time-periods are processed, then gas emission parameter accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-processing spectral signals from multiple bands and time-periods before they are input to the deep-learning classification models. This includes extracting spectral features, preparing training data, and optimizing model inputs in advance, which reduces the actual processing time required during gas emission estimation while maintaining high accuracy
Solution Approach 2:
The patent replaces traditional sequential processing methods with parallel deep-learning inference that can process multiple spectral bands and time-periods simultaneously. This substitution dramatically reduces processing time while maintaining the accuracy benefits of multi-band, multi-temporal analysis, as the neural network can handle complex multi-dimensional inputs efficiently
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and automated detection and quantification of gas emissions, such as methane and carbon dioxide, over large geographical areas, providing high-resolution measurements and alerts when thresholds are exceeded.
Implementation Method 1
obtaining, by a computing node and from one or more overhead sensors, one or more spectral signals over the geospatial area in three or more different spectral bands
Data Source
AI summary
The present disclosure relates a technique for determining gas emission parameters over a geospatial area. The system includes a non-transitory computer-readable medium for storing the spectral signals obtained from overhead sensors and one or more trained deep-learning classification models. The system further includes one or more processors configured to determine one or more gas emission parameters over the geospatial area based on the one or more spectral signals using the one or more trained deep-learning classification models. Each of the one or more trained deep-learning classification models is generated by generating training data based on training samples representative of spectral signals from the one or more geospatial areas at two or more different time-periods, forming a set of training data batches, and training a deep-learning classification model based on the set of training data batches by applying an iterative optimization procedure to adjust hyperparameters of the deep learning classification model.