RESOURCE EFFICIENT TRAINING OF MACHINE LEARNING MODELS THAT PREDICT STOCHASTIC SPREAD

    公开(公告)号:US20240135691A1

    公开(公告)日:2024-04-25

    申请号:US18493018

    申请日:2023-10-23

    CPC classification number: G06V10/776 G06V10/761

    Abstract: Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

    WILDFIRE IDENTIFICATION IN IMAGERY
    2.
    发明公开

    公开(公告)号:US20240331518A1

    公开(公告)日:2024-10-03

    申请号:US18589385

    申请日:2024-02-27

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying wildfire in satellite imagery. In some implementations, a server obtains a satellite image of a geographic region and a date corresponding to when the satellite image was generated. The server determines a number of pixels in the satellite image that are indicated as on fire. The server obtains satellite imagery of the geographic region from before the date. The server generates a statistical distribution from the satellite imagery. The server determines a likelihood that the satellite image illustrates fire based on a comparison of the determined number of pixels in the satellite image that are indicated as on fire to the generated statistical distribution. The server can compare the determined likelihood to a threshold. In response to comparing the determined likelihood to the threshold, the server provides an indication that the satellite image illustrates fire.

    Wildfire identification in imagery

    公开(公告)号:US12288455B2

    公开(公告)日:2025-04-29

    申请号:US18589385

    申请日:2024-02-27

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying wildfire in satellite imagery. In some implementations, a server obtains a satellite image of a geographic region and a date corresponding to when the satellite image was generated. The server determines a number of pixels in the satellite image that are indicated as on fire. The server obtains satellite imagery of the geographic region from before the date. The server generates a statistical distribution from the satellite imagery. The server determines a likelihood that the satellite image illustrates fire based on a comparison of the determined number of pixels in the satellite image that are indicated as on fire to the generated statistical distribution. The server can compare the determined likelihood to a threshold. In response to comparing the determined likelihood to the threshold, the server provides an indication that the satellite image illustrates fire.

    Wildfire identification in imagery

    公开(公告)号:US12080137B2

    公开(公告)日:2024-09-03

    申请号:US17368256

    申请日:2021-07-06

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying wildfire in satellite imagery. In some implementations, a server obtains a satellite image of a geographic region and a date corresponding to when the satellite image was generated. The server determines a number of pixels in the satellite image that are indicated as on fire. The server obtains satellite imagery of the geographic region from before the date. The server generates a statistical distribution from the satellite imagery. The server determines a likelihood that the satellite image illustrates fire based on a comparison of the determined number of pixels in the satellite image that are indicated as on fire to the generated statistical distribution. The server can compare the determined likelihood to a threshold. In response to comparing the determined likelihood to the threshold, the server provides an indication that the satellite image illustrates fire.

    RESOURCE EFFICIENT TRAINING OF MACHINE LEARNING MODELS THAT PREDICT STOCHASTIC SPREAD

    公开(公告)号:US20240233346A9

    公开(公告)日:2024-07-11

    申请号:US18493018

    申请日:2023-10-24

    CPC classification number: G06V10/776 G06V10/761

    Abstract: Methods, systems, and apparatus for obtaining input features representative of a region of space, processing an input comprising the input features through the ML model to generate a prediction describing predicted features of the region of space, obtaining result features describing the region of space, determining a value of at least one evaluation metric that relates the predicted features and the result features, that at least one evaluation metric including one of a distance score, a pyramiding density error, and min-max intersection over union (IOU) score, and training the ML model responsive to the at least one evaluation metric. Other implementations of this aspect include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices.

    TEMPORAL BOUNDS OF WILDFIRES
    6.
    发明申请

    公开(公告)号:US20250104208A1

    公开(公告)日:2025-03-27

    申请号:US18787405

    申请日:2024-07-29

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a temporal range of a fire. In some implementations, a server obtains a date when a fire occurred within a region. The server obtains satellite imagery of the region from before the date when the fire occurred. The server generates a first statistical distribution from the satellite imagery. The server determines a start date of the fire using the first statistical distribution. The server obtains second satellite imagery of the region from before and after the start date. The server selects a second set of imagery from the second satellite imagery from before the start date. The server generates a second statistical distribution from the second set of imagery. The server determines an end date of the fire using the second statistical distribution. The server provides the start date and the end date for output.

    Temporal bounds of wildfires
    7.
    发明授权

    公开(公告)号:US12051181B2

    公开(公告)日:2024-07-30

    申请号:US17354842

    申请日:2021-06-22

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a temporal range of a fire. In some implementations, a server obtains a date when a fire occurred within a region. The server obtains satellite imagery of the region from before the date when the fire occurred. The server generates a first statistical distribution from the satellite imagery. The server determines a start date of the fire using the first statistical distribution. The server obtains second satellite imagery of the region from before and after the start date. The server selects a second set of imagery from the second satellite imagery from before the start date. The server generates a second statistical distribution from the second set of imagery. The server determines an end date of the fire using the second statistical distribution. The server provides the start date and the end date for output.

Patent Agency Ranking