Artificial intelligence system for identifying and assessing attributes of a property shown in aerial imagery

A computer-based method comprising:

• receiving, at a computer-based system, an aerial image of a property that includes a first visual indicator on the aerial image that follows and identifies a boundary line for the property;
• using a building rooftop deep fully convolutional network, configured and trained to predict the presence of building rooftops in aerial imagery, to predict whether any building rooftops are present within the boundary line of the property based on the aerial image;
• applying a second visual indicator to the aerial image to identify and outline a building rooftop in the aerial image identified by the building rooftop deep fully convolutional network,
• providing an above-ground pool deep fully convolutional network, an in-ground pool deep fully convolutional network, and a trampoline deep fully convolutional network,
• wherein each of the above-ground pool deep fully convolutional network, the in-ground pool deep fully convolutional network, and the trampoline deep fully convolutional network comprises:
• an encoder,
• a decoder, and
• a plurality of skip connections that extend from various points in the encoder to the decoder,
• wherein the encoder comprises:
• an input,
• a normalization layer, and
• ten processing segments;
• and
• wherein the decoder comprises:
• eight processing segments;
• and
• an output,
• wherein every processing segment in the encoder and decoder comprises:
• a convolution layer,
• a batch normalization layer;
• and
• an exponential linear unit (elu) layer, and
• wherein every other processing segment in the encoder, starting with a second processing segment in the encoder, has a max pooling layer.

A computer-based method comprising:

• receiving, at a computer-based system, an aerial image of a property that includes a first visual indicator on the aerial image that follows and identifies a boundary line for the property;
• using a building rooftop deep fully convolutional network, configured and trained to predict the presence of building rooftops in aerial imagery, to predict whether any building rooftops are present within the boundary line of the property based on the aerial image;
• applying a second visual indicator to the aerial image to identify and outline a building rooftop in the aerial image identified by the building rooftop deep fully convolutional network,
• wherein the building rooftop deep fully convolutional network comprises:
• an encoder,
• a decoder, and
• a plurality of skip connections that extend from various points in the encoder to the decoder,
• wherein the encoder comprises:
• an input,
• a normalization layer, and
• ten processing segments;
• and
• wherein the decoder comprises:
• eight processing segments;
• and
• an output,
• wherein every processing segment in the encoder and decoder comprises:
• a dilated convolution layer,
• a batch normalization layer;
• and
• an exponential linear unit (elu) layer, and
• wherein every other processing segment in the encoder, starting with a second processing segment in the encoder, has a max pooling layer.

A computer-based system comprising:

• a network interface connection for receiving a plurality of aerial images from a data source over a network, wherein each image has a first visual indicator on the aerial image that follows and identifies a boundary line for the property;
• a building rooftop deep fully convolutional network, configured and trained to predict the presence of building rooftops in aerial imagery, and apply a second visual indicator to the aerial image to identify and outline a building rooftop in the aerial image identified by the building rooftop deep fully convolutional network, and
• an additional deep fully convolutional network, configured and trained to predict the presence of an additional property feature in aerial imagery, and predict whether said additional property feature is present with the boundary line of the property based on the aerial image,
• wherein the building rooftop and additional deep convolutional networks have identical configurations but use different weights.