US 12,737,515 B2Granted

Estimating emission source location from satellite imagery

AssigneeInternational Business Machines Corporation

InventorsTheodore G. van Kessel, Levente Klein, Bruce Gordon Elmegreen, Eloisa Bentivegna

Life of this patent

Estimating the term…

Loading patent…

Abstract

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.

Claims19

Dependent claims

A computer-implemented method comprising:

creating a dataset of plume concentration data by combining high-resolution synthetic plume data generated from a synthetic plume model having a higher spatial and temporal resolution than satellite data and a modeled distribution of actual plume data using satellite weather data, the dataset including synthetic plumes, corresponding positions and magnitudes, and the satellite weather data;
responsive to the high-resolution synthetic plume data having the higher spatial and temporal resolution, down sampling the dataset from the high-resolution synthetic plume data to a lower resolution array of synthetic plume data at satellite resolution, the dataset arranged as a first array of pixels with a plume at or near a center pixel of the first array;
partitioning the dataset of plume concentration data into two separate datasets according to a preset proportion as a training dataset and a validation dataset;
preparing the training dataset by recoding position entries of the plume as zero when a source of the plume is disposed outside the center pixel and as one for the source of the plume is contained in the center pixel;
training two machine learning models on at least one of the two separate datasets by:
training a first machine learning model to identify a presence of plumes;
and
training a second machine learning model to identify a source position and magnitude of the identified plumes;
applying the two machine learning models to each pixel, in sequence, of a data patch of a new set of actual plume concentration data to identify an actual plume, wherein the second machine learning model is applied responsive to the first machine learning model identifying the presence of a first plume at the pixel;
responsive to receiving an output from the second machine learning model of a subpixel position of a first plume source detected by the first machine learning model at a respective pixel, mathematically combining the respective pixel and the subpixel position to form a single coordinate;
and
logging the single coordinate of the actual plume to identify a position of the first plume.

9 more elements

A computer program product comprising:

one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising:
program instructions to create a dataset of plume concentration data by combining high-resolution synthetic plume data generated from a synthetic plume model having a higher spatial and temporal resolution than satellite data and a modeled distribution of actual plume data using satellite weather data, the dataset including synthetic plumes, corresponding positions and magnitudes, and the satellite weather data;
program instructions to, responsive to the high-resolution synthetic plume data having the higher spatial and temporal resolution, down sample the dataset from the high-resolution synthetic plume data to a lower resolution array of synthetic plume data at satellite resolution, the dataset arranged as a first array of pixels with a plume at or near a center pixel of the first array;
program instructions to partition the dataset of plume concentration data into two separate datasets according to a preset proportion as a training dataset and a validation dataset;
program instructions to prepare the training dataset by recoding position entries of the plume as zero when a source of the plume is disposed outside the center pixel and as one for the source of the plume is contained in the center pixel;
program instructions to train two machine learning models on at least one of the two separate datasets by:
training a first machine learning model to identify a presence of plumes;
and
training a second machine learning model to identify a source position and magnitude of the identified plumes;
program instructions to apply the two machine learning models to each pixel, in sequence, of a data patch of a new set of actual plume concentration data to identify an actual plume, wherein the second machine learning model is applied responsive to the first machine learning model identifying the presence of a first plume at the pixel;
responsive to receiving an output from the second machine learning model of a subpixel position of a first plume source detected by the first machine learning model at a respective pixel, mathematically combine the respective pixel and the subpixel position to form a single coordinate;
and
logging the single coordinate of the actual plume to identify a position of the first plume.

10 more elements

A computer system comprising:

one or more computer processors;
one or more computer readable storage media;
and
program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the stored program instructions comprising:
program instructions to create a dataset of plume concentration data by combining high-resolution synthetic plume data generated from a synthetic plume model having a higher spatial and temporal resolution than satellite data and a modeled distribution of actual plume data using satellite weather data, the dataset including synthetic plumes, corresponding positions and magnitudes, and the satellite weather data;
program instructions to, responsive to the high-resolution synthetic plume data having the higher spatial and temporal resolution, down sample the dataset from the high-resolution synthetic plume data to a lower resolution array of synthetic plume data at satellite resolution, the dataset arranged as a first array of pixels with a plume at or near a center pixel of the first array;
program instructions to partition the dataset of plume concentration data into two separate datasets according to a preset proportion as a training dataset and a validation dataset;
program instructions to prepare the training dataset by recoding position entries of the plume as zero when a source of the plume is disposed outside the center pixel and as one for the source of the plume is contained in the center pixel;
program instructions to train two machine learning models on at least one of the two separate datasets by:
training a first machine learning model to identify a presence of plumes;
and
training a second machine learning model to identify a source position and magnitude of the identified plumes;
program instructions to apply the two machine learning models to each pixel, in sequence, of a data patch of a new set of actual plume concentration data to identify an actual plume, wherein the second machine learning model is applied responsive to the first machine learning model identifying the presence of a first plume at the pixel;
responsive to receiving an output from the second machine learning model of a subpixel position of a first plume source detected by the first machine learning model at a respective pixel, mathematically combine the respective pixel and the subpixel position to form a single coordinate;
and
logging the single coordinate of the actual plume to identify a position of the first plume.

13 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/809,037Filed 27 Jun 2022
Published: US 2023/0418999 A128 Dec 2023
Granted: 15 Sep 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification16 codes

G06F 30/27
Design optimisation, verification or simulation: using machine learning, e.g. artificial intelligence, neural networks, support vector machines…
G06F 30/28
Design optimisation, verification or simulation: using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/08
Neural networks: learning methods