20240044783. GENERATING HIGH-RESOLUTION CONCENTRATION MAPS FOR ATMOSPHERIC GASES USING GEOGRAPHY-INFORMED MACHINE LEARNING simplified abstract (Palo Alto Research Center Incorporated)

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GENERATING HIGH-RESOLUTION CONCENTRATION MAPS FOR ATMOSPHERIC GASES USING GEOGRAPHY-INFORMED MACHINE LEARNING

Organization Name

Palo Alto Research Center Incorporated

Inventor(s)

Kalaivani Ramea Kubendran of Fremont CA (US)

Md Nurul Huda of Hillsboro OR (US)

David Schwartz of Concord MA (US)

Jeyasri Subramanian of Sunnyvale CA (US)

GENERATING HIGH-RESOLUTION CONCENTRATION MAPS FOR ATMOSPHERIC GASES USING GEOGRAPHY-INFORMED MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240044783 titled 'GENERATING HIGH-RESOLUTION CONCENTRATION MAPS FOR ATMOSPHERIC GASES USING GEOGRAPHY-INFORMED MACHINE LEARNING

Simplified Explanation

The patent application describes a method for generating high-resolution atmospheric gas concentration maps using machine learning and remote sensing data. Here is a simplified explanation of the patent application:

  • The method starts by obtaining a remote sensing dataset that includes atmospheric gas concentration data for multiple gases within a specific geographic area.
  • A training dataset is generated based on the remote sensing dataset, which will be used to train a machine learning model.
  • The machine learning model is trained using the training dataset to predict atmospheric gas concentration values for the given geographic area.
  • The spatial resolution of the predicted gas concentration values is higher than the spatial resolution of the input data.
  • The trained model can be used to generate one or more high-resolution atmospheric gas concentration maps for the geographic area.

Potential applications of this technology:

  • Environmental monitoring: The high-resolution gas concentration maps can be used to monitor air quality and identify areas with high pollution levels.
  • Climate research: The maps can provide valuable data for studying the distribution and movement of atmospheric gases, contributing to climate change research.
  • Urban planning: The maps can help urban planners understand the spatial distribution of gases and make informed decisions regarding infrastructure development and pollution control measures.

Problems solved by this technology:

  • Limited spatial resolution: Traditional remote sensing data may have lower spatial resolution, making it challenging to accurately assess gas concentrations in specific areas.
  • Data availability: Remote sensing data may not be readily available for all geographic areas, especially at high resolutions.
  • Time-consuming analysis: Manual analysis of remote sensing data to estimate gas concentrations can be time-consuming and labor-intensive.

Benefits of this technology:

  • High-resolution mapping: The method allows for the generation of high-resolution gas concentration maps, providing detailed information about gas distribution in specific areas.
  • Efficiency: By using machine learning, the process of predicting gas concentrations is automated, saving time and effort compared to manual analysis.
  • Geographic specificity: The method can generate gas concentration maps for specific geographic areas, allowing for targeted analysis and decision-making.


Original Abstract Submitted

generating one or more high-resolution atmospheric gas concentration maps using geography-informed machine learning includes obtaining a remote sensing dataset constrained by at least one temporal window and at least one spatial window defining a first geographic area. the remote sensing dataset includes at least a first set of atmospheric gas concentration data for a plurality of atmospheric gases. a training dataset is generated based on the remote sensing dataset. a machine learning model is trained with the training dataset to predict a plurality of atmospheric gas concentration values for at least one atmospheric gas of the plurality of atmospheric gases in a given geographic area and with a spatial resolution that is greater than a spatial resolution of atmospheric gas concentration data provided as an input to the machine learning module.

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