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

1. 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; and

using an additional deep fully convolutional network, configured and trained to predict the presence of an additional property feature in aerial imagery, to 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.

2. The computer-based method of claim 1 , further comprising:

using a skylights deep fully convolutional network, configured and trained to predict the presence of skylights on building rooftops in aerial imagery, to predict whether any skylights are present on the building rooftop that was identified by the second visual indicator;

using a solar panels deep fully convolutional network, configured and trained to predict the presence of solar panels on building rooftops in aerial imagery, to predict whether any solar panels are present on the building rooftop that was identified by the second visual indicator; and/or

using a vegetation overhang deep fully convolutional network, configured and trained to predict the presence of vegetation overhanging building rooftops in aerial imagery, to predict whether any vegetation is overhanging the building rooftop that was identified by the second visual indicator.

3. The computer-based method of claim 2 , further comprising:

applying a third visual indicator to the aerial image to identify and outline skylight(s) identified by the skylights deep fully convolutional network on the building rooftop;

applying a fourth visual indicator to the aerial image to identify and outline solar panel(s) identified by the solar panels deep fully convolutional network on the building rooftop; and/or

applying a fifth visual indicator to the aerial image to identify and outline vegetation overhang identified by the vegetation overhang deep fully convolutional network on the building rooftop.

4. The computer-based method of claim 3 , wherein the skylights deep fully convolutional network, the solar panels deep fully convolutional network, and the vegetation overhang deep fully convolutional network have identical configurations but use different weights.

5. The computer-based method of claim 4 , further comprising:

providing each of the skylights deep fully convolutional network, solar panels deep fully convolutional network, and vegetation overhang deep fully convolutional network,

wherein each of the skylights deep fully convolutional network, solar panels deep fully convolutional network, and vegetation overhang 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.

6. The computer-based method of claim 5 , wherein:

each of the first two convolution layers in the encoder is associated with a feature vector that represents 32 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 64 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 128 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 256 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 512 features, and

wherein:

each of the first two convolution layers in the decoder is associated with a feature vector that represents 256 features,

each of the next two convolution layers in the decoder is associated with a feature vector that represents 128 features,

each of the next two convolution layers in the decoder is associated with a feature vector that represents 64 features, and

the last two dilated convolution layers in the decoder are associated with a feature vector that represents 32 features, and

wherein:

a first of the skip connections extends from a point in the encoder just before a first of the max pooling layers to an output of a final processing segment in the decoder,

a second of the skip connections extends from a point in the encoder just before a second of the max pooling layers to a point in the decoder just before a last two of the processing segments in the decoder,

a third of the skip connections extends from a point in the encoder just before a third of the max pooling layers to a point in the decoder just before a last four of the processing segments in the decoder, and

a fourth of the skip connections extends from a point in the encoder just before a last of the max pooling layers to a point in the decoder just after a first two of the processing segments in the decoder.

7. The computer-based method of claim 1 , further comprising applying a version of the aerial image to one or more of a plurality of image classifiers, wherein each image classifier is configured to classify one or more attributes in the aerial image.

8. The computer-based method of claim 1 , further comprising:

classifying, with a vegetation overhang classifier, a degree to which vegetation in the aerial image is overhanging the building rooftop identified by the building rooftop deep fully convolutional network;

classifying, with a roof condition classifier, a condition of the building rooftop identified by the building rooftop deep full convolutional network;

classifying, with a roof material classifier, a material of the building rooftop identified by the building rooftop deep fully convolutional network; and/or

classifying, with a roof type classifier, a roof type for the building rooftop identified by the building rooftop deep fully convolutional network.

9. The computer-based method of claim 8 , wherein classifying the vegetation overhang comprises:

calculating an area of the building rooftop identified and outlined by the second visual indicator in the aerial image;

identifying, with a vegetation overhang deep fully convolutional network, vegetation overhanging the building rooftop identified and outlined by the second visual indicator in the aerial image;

calculating an area of the vegetation overhang identified by the vegetation overhang deep fully convolutional network;

calculating a percentage of the building rooftop covered by the vegetation overhang based on the calculated building rooftop area and the vegetation overhang area; and

applying the calculated percentage to decision tree logic to determine the degree to which vegetation in the aerial image is overhanging the building rooftop identified by the building rooftop deep fully convolutional network.

10. The computer-based method of claim 8 , wherein classifying the roof condition comprises:

using a building rooftop staining deep convolutional neural network, configured and trained to predict whether staining is present on building rooftops in aerial imagery, to identify a probability that staining is present on the building rooftop identified and outlined by the second visual indicator in the aerial image;

using a building rooftop surface deterioration deep convolutional neural network, configured and trained to predict whether surface deterioration is present on building rooftops in aerial imagery, to identify that surface deterioration is present on the building rooftop identified and outlined by the second visual indicator in the aerial image;

using the probability that staining is present on the building rooftop identified and outlined by the second visual indicator in the aerial image, and the probability that surface deterioration is present on the building rooftop identified and outlined by the second visual indicator in the aerial image to calculate a disrepair index for the building rooftop; and

applying the calculated disrepair index to decision tree logic to determine the condition of the building rooftop identified by the building rooftop deep fully convolutional network.

11. The computer-based method of claim 8 , wherein classifying the building rooftop material comprises:

using building rooftop material deep convolutional neural networks, configured and trained to identify probabilities that the building rooftop has different types of roof material from aerial imagery, to identify probabilities that the building rooftop includes a plurality of different materials, respectively; and

selecting, with a computer-based selector, a top one or two, highest probability roof materials.

12. The computer-based method of claim 8 , wherein classifying the building roof type comprises:

using building rooftop type deep convolutional neural networks, configured and trained to identify probabilities that the building rooftop has different possible types from aerial imagery, to identify probabilities that the building rooftop has different types, respectively; and

selecting, with a computer-based selector, a top one or two, highest probability roof types.

13. The computer-based method of claim 1 , further comprising:

using an above-ground pool deep fully convolutional network, configured and trained to predict the presence of above-ground pools in aerial imagery, to predict whether any above-ground pools are present within the boundary line of the property based on the aerial image; and

applying a third visual indicator to the aerial image to identify and outline an above-ground pool in the aerial image identified by the above-ground pool deep fully convolutional network.

14. The computer-based method of claim 13 , further comprising:

using an in-ground pool deep fully convolutional network, configured and trained to predict the presence of in-ground pools in aerial imagery, to predict whether any in-ground pools are present within the boundary line of the property based on the aerial image; and

applying a fourth visual indicator to the aerial image to identify and outline an in-ground pool in the aerial image identified by the in-ground pool deep fully convolutional network.

15. The computer-based method of claim 14 , further comprising:

using a trampoline deep fully convolutional network, configured and trained to predict the presence of trampolines in aerial imagery, to predict whether any trampolines are present within the boundary line of the property based on the aerial image; and

applying a fifth visual indicator to the aerial image to identify and outline a trampoline in the aerial image identified by the trampoline deep fully convolutional network.

16. The computer-based method of claim 15 , wherein the above-ground pool deep fully convolutional network, the in-ground pool deep fully convolutional network, and the trampoline deep fully convolutional network have identical configurations but use different weights.

17. 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.

18. The computer-based method of claim 17 , wherein:

each of the first two convolution layers in the encoder is associated with a feature vector that represents 16 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 32 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 64 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 128 features,

each of the next two convolution layers in the encoder is associated with a feature vector that represents 256 features, and

wherein:

each of the first two convolution layers in the decoder is associated with a feature vector that represents 128 features,

each of the next two convolution layers in the decoder is associated with a feature vector that represents 64 features,

each of the next two convolution layers in the decoder is associated with a feature vector that represents 32 features, and

the last two dilated convolution layers in the decoder are associated with a feature vector that represents 16 features, and

wherein:

a first of the skip connections extends from a point in the encoder just before a first of the max pooling layers to an output of a final processing segment in the decoder,

a second of the skip connections extends from a point in the encoder just before a second of the max pooling layers to a point in the decoder just before a last two of the processing segments in the decoder,

a third of the skip connections extends from a point in the encoder just before a third of the max pooling layers to a point in the decoder just before a last four of the processing segments in the decoder, and

a fourth of the skip connections extends from a point in the encoder just before a last of the max pooling layers to a point in the decoder just after a first two of the processing segments in the decoder.

19. The computer-based method of claim 1 , further comprising:

enabling a first human annotator to annotate the received image, at a computer-based user terminal, to specify whether visual inspection by the first human annotator revealed, in the aerial image, any building rooftops, in-ground pools, above-ground pools, trampolines, skylights, solar panels, vegetation overhang, staining, surface deterioration, specific roof materials, and/or specific roof types; and

comparing the annotation(s) made by the first human annotator to any determinations or predictions made by the computer-based system.

20. The computer-based method of claim 19 , further comprising:

if the comparison reveals a discrepancy between the annotations made by the first human annotator and the determinations or predictions made by the computer-based system, presenting the aerial image to a second human annotator who is different than the first human annotator.

21. The computer-based method of claim 1 , further comprising:

masking and resizing the aerial image prior to using the building rooftop deep fully convolutional network to predict whether any building rooftops are present within the boundary line of the property based on the aerial image.

22. The computer-based method of claim 21 , wherein masking and resizing is performed for all property and rooftop segmentation deep fully convolutional network models.

23. The computer-based method of claim 1 , wherein using the building rooftop deep fully convolutional network produces a probability image version of the aerial image, wherein the probability image is a pixel-by-pixel representation of whether the aerial image shows a building rooftop.

24. The computer-based method of claim 23 , wherein all property and rooftop segmentation deep fully convolutional network models produce a probability image version of the areal image.

25. The computer-based method of claim 23 , further comprising:

thresholding and resizing the probability image; and

applying connected components analysis and an area filter to produce a final output that is a version of the aerial image that includes the second visual indicator identifying and outlining the building rooftop in the aerial image.

26. The computer-based method of claim 25 , wherein all property and rooftop segmentation deep fully convolutional network models threshold and resize the probability image and apply connection components and area filtering to produce a final output.

27. 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.

28. The computer-based method of claim 27 , wherein:

each of the first two dilated convolution layers in the encoder is associated with a feature vector that represents 64 features and a 2×2 dilation kernel,

each of the next two dilated convolution layers in the encoder is associated with a feature vector that represents 128 features and a 4×4 dilation kernel,

each of the next two dilated convolution layers in the encoder is associated with a feature vector that represents 256 features and an 8×8 dilation kernel,

each of the next two dilated convolution layers in the encoder is associated with a feature vector that represents 512 features and a 16×16 dilation kernel,

each of the next two dilated convolution layers in the encoder is associated with a feature vector that represents 1024 features using a 32×32 dilation kernel, and

wherein:

each of the first two dilated convolution layers in the decoder is associated with a feature vector that represents 512 features and a 16×16 dilation kernel,

each of the next two dilated convolution layers in the decoder is associated with a feature vector that represents 256 features and a 8×8 dilation kernel,

each of the next two dilated convolution layers in the decoder is associated with a feature vector that represents 128 features and a 4×4 dilation kernel, and

the last two dilated convolution layers in the decoder are associated with a feature vector that represents 64 features and a 2×2 dilation kernel, and

wherein:

a first of the skip connections extends from a point in the encoder just before a first of the max pooling layers to an output of a final processing segment in the decoder,

a second of the skip connections extends from a point in the encoder just before a second of the max pooling layers to a point in the decoder just before a last two of the processing segments in the decoder,

a third of the skip connections extends from a point in the encoder just before a third of the max pooling layers to a point in the decoder just before a last four of the processing segments in the decoder, and

a fourth of the skip connections extends from a point in the encoder just before a last of the max pooling layers to a point in the decoder just after a first two of the processing segments in the decoder.

29. 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.