17809037. ESTIMATING EMISSION SOURCE LOCATION FROM SATELLITE IMAGERY simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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ESTIMATING EMISSION SOURCE LOCATION FROM SATELLITE IMAGERY

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Theodore G. Van Kessel of Millbrook NY (US)

LEVENTE Klein of Tuckahoe NY (US)

Bruce Gordon Elmegreen of Goldens Bridge NY (US)

Eloisa Bentivegna of Warrington (GB)

ESTIMATING EMISSION SOURCE LOCATION FROM SATELLITE IMAGERY - A simplified explanation of the abstract

This abstract first appeared for US patent application 17809037 titled 'ESTIMATING EMISSION SOURCE LOCATION FROM SATELLITE IMAGERY

Simplified Explanation

The patent application describes a method for estimating the location of emission sources using satellite plume data. Here are the key points:

  • The method involves creating a dataset of plume concentration data from satellite observations.
  • The dataset is then downsampled to match the resolution of the satellite.
  • The downscaled dataset is divided into two separate datasets based on a predetermined proportion.
  • Two machine learning models are trained using at least one of the two datasets.
  • The first model is designed to identify the presence of a plume, while the second model identifies the position and magnitude of the plume source.
  • The trained models are then applied to new concentration data to estimate the emission source location.

Potential applications of this technology:

  • Environmental monitoring: The method can be used to track and identify emission sources, helping in the monitoring and regulation of air pollution.
  • Industrial emissions control: Industries can use this method to locate and quantify their emission sources, enabling them to take necessary measures to reduce pollution.
  • Emergency response: The method can aid in quickly identifying the source of hazardous emissions during emergencies, allowing for prompt action to protect public health.

Problems solved by this technology:

  • Traditional methods of estimating emission source locations are often time-consuming and require extensive manual analysis.
  • This method automates the process using machine learning, making it more efficient and accurate.
  • It provides a scalable solution for analyzing large amounts of satellite plume data.

Benefits of this technology:

  • Improved accuracy: The use of machine learning models enhances the accuracy of estimating emission source locations.
  • Time and cost savings: The automated process reduces the need for manual analysis, saving time and resources.
  • Scalability: The method can handle large datasets, making it suitable for analyzing satellite plume data on a global scale.


Original Abstract Submitted

In an approach for estimating emission source location from satellite plume data, a processor creates a dataset of plume concentration data. A processor down samples the dataset to an array at satellite resolution. A processor partitions the array into two separate datasets according to a preset proportion. A processor trains two machine learning models on at least one of the two separate datasets, wherein a first machine learning model of the two machine learning models is for identifying a presence of a plume and a second machine learning model of the two machine learning models is for identifying a source position and magnitude of the plume. A processor applies the two machine learning models to new concentration data.

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