Systems and methods for generating flood hazard estimation using machine learning model and satellite data

Number of patents in Portfolio can not be more than 2000

United States of America

PATENT NO 12236505
APP PUB NO 20220108504A1
SERIAL NO

17493788

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Abstract

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A system and method for flood hazard estimation inputs a satellite elevation map and applies a machine learning model to output a geographic map representing flood hazard areas. The machine learning model is trained using a generative adversarial network (GAN) to produce an output of a deterministic hazard mapping algorithm. A GAN objective applies a loss function, reweighted to increase the importance of high hazard areas. The method retrieves a DEM topography file representing elevation data of an identified terrain, and applies a sink-filling algorithm to detect and fill sinks in the DEM topography. The algorithm subtracts the DEM elevation data to generate a filled topography, and identifies flattest regions of the filled topography. The algorithm then generates a flood hazard map by merging the filled topography and the DEM elevation data, using a weighting function that balances the detected sinks and the flattest regions of the filled topography.

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Patent Owner(s)

  • BANK OF MONTREAL

International Classification(s)

Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Fraser, Todd Toronto, CA 3 12
Kasiri, Keyvan Toronto, CA 1 2
Kenyon-Dean, Kian Toronto, CA 2 2
Morrison, Lyndsay Toronto, CA 1 2
Torrance, Michael Toronto, CA 3 13
Vahlis, Yevgeniy Toronto, CA 1 2
Wu, Stella Toronto, CA 1 2
Zhao, Bo Toronto, CA 183 896

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