Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.