Platform, systems, and methods for identifying property characteristics and property feature maintenance through aerial imagery analysis

• US 10,529,029 B2
• Filed: 09/25/2017
• Issued: 01/07/2020
• Est. Priority Date: 09/23/2016
• Status: Active Grant

• Current Assignee: AON Benfield Incorporated (Aon Plc (Spain))
• Original Assignee: AON Benfield Incorporated (Aon Plc (Spain))
• Inventors: Okazaki, Takeshi
• Primary Examiner(s): Nguyen, Tien C
• Application Number:
  US15/714,376
• Publication Number:
  US 20180089763A1
• Time in Patent Office: 834 Days
• Field of Search: 705 2, 705 3, 705 4, 705 35, 705 39, 705 38, 705 37, 701516, 382103, 34053911, 707736, 348 47
• CPC Class Codes: G06F 16/51 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 18/2414 Smoothing the distance, e.g. radial basis function networks [RBFN]
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2210/04 Architectural design, interior design
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/176 Urban or other man-made structures
• CPC Class Codes: G06V 20/20 in augmented reality scenes

Abstract

In an illustrative embodiment, methods and systems for automatically categorizing a repair condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification. The methods and systems may further include determining, using the property characteristic classification and the condition classification, a risk estimate of damage to the property due to one or more disasters and/or a cost estimate of repair or replacement of the property characteristic.

Claims

1. A method for automatically categorizing a repair condition of a property characteristic, comprising:

receiving, from a user at a remote computing device, a request for a property condition classification, wherein the property classification request includes identification of a property and at least one property characteristic;

obtaining, by processing circuitry of a computing system responsive to receiving the request, an aerial image of a geographic region including the property;

extracting, by the processing circuitry, one or more of a plurality of features from the aerial image corresponding to the property characteristic, wherein the extracted features include pixel groupings representing the property characteristic;

determining, by the processing circuitry from the extracted features, a property characteristic classification for the property characteristic, wherein determining the property characteristic classification includes applying the pixel groupings for the property characteristic to a first machine learning classifier trained to identify property characteristics from a set of pixel groupings;

determining, by the processing circuitry based on the identified property characteristic and the extracted features, a condition classification for the property characteristic, wherein identifying the condition classification includes applying the pixel groupings for the property characteristic to a second machine learning classifier trained to identify property characteristic conditions from a set of pixel groupings;

determining, by the processing circuitry based in part on the property characteristic classification and the condition classification, a risk estimate of damage to the property due to one or more disasters; and

returning, to the user at the remote computing device via a graphical user interface responsive to receiving the request, a condition assessment of the property characteristic including the condition classification and the risk estimate of damage to the property due to the one or more disasters.

2. The method of claim 1, wherein the property characteristic classification is rooftop shape.

3. The method of claim 2, wherein the first machine learning classifier is trained with a set of aerial training images having known rooftop shapes such that the machine learning classifier is configured to determine the rooftop shape from a plurality of rooftop shapes.

4. The method of claim 1, wherein at least one of the first machine learning classifier and the second machine learning classifier is a Network in Network (NIN) classifier.

5. The method of claim 1, wherein determining the risk estimate comprises applying a disaster risk profile corresponding to a) a first disaster of the at least one disaster and b) the property characteristic.

6. The method of claim 1, wherein the pixel groupings comprise at least one of angles, outlines, and substantially homogenous pixel fields defining the property characteristic.

7. The method of claim 1, wherein identifying the condition classification for the property characteristic comprises:

generating, from the pixel groupings, a pixel intensity distribution of the property characteristic; and

determining, by applying the second machine learning classifier, the condition classification based on the pixel intensity distribution of the property characteristic.

8. A system for automatically categorizing a repair condition of a property characteristic, comprising:

processing circuitry; and

a non-transitory computer-readable medium having instructions stored thereon;

wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to

receive, from a user at a remote computing device, a property condition classification request, wherein the property classification request includes identification of a property and at least one property characteristic;

obtain, from a remote data source responsive to receiving the property classification request, an aerial image of a geographic region including the property;

extract one or more of a plurality of features from the aerial image corresponding to the property characteristic, wherein the extracted features include pixel groupings representing the property characteristic;

determine, from the extracted features, a property characteristic classification, wherein determining the property characteristic classification includes applying the pixel groupings for the property characteristic to a first machine learning classifier trained to identify property characteristics from a set of pixel groupings;

determine, based on the identified property characteristic and the extracted features, a condition classification for the property characteristic, wherein identifying the condition classification includes applying the pixel groupings for the property characteristic to a second machine learning classifier trained to identify property characteristic conditions from a set of pixel groupings; and

determine, in real-time responsive to receiving the property classification request and using the property characteristic classification and the condition classification, a replacement cost for replacing the property characteristic.

9. The system of claim 8, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to, prior to identifying the one or more of the plurality of features:

obtain a shape map image including the property;

overlay the aerial image with the shape map image; and

determine whether a boundary of the property as identified by the shape map matches a boundary of the property as illustrated in the aerial image.

10. The system of claim 9, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to, upon determining that the shape map does not match the boundary of the corresponding property, obtain an alternate aerial image of the property.

11. The system of claim 8, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to compare a current condition classification of the property characteristic to a stored historic condition classification of the property characteristic.

12. The system of claim 11, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to, in real-time responsive to receiving the property condition classification request, determine whether repairs have been made to the property based on the comparison of the current condition classification to the historic condition classification of the property characteristic.

13. The system of claim 8, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to, prior to identifying the one or more of the plurality of features, assess orthogonality of the aerial imagery.

14. The system of claim 13, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to, responsive to assessing that the orthogonality of the aerial imagery does not correspond to a true orthophoto form, cause correction of the aerial imagery to the true orthophoto form.

15. A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by processing circuitry, cause the processing circuitry to:

receive, from a user at a remote computing device, a property condition classification request, wherein the property classification request includes identification of a property and at least one property characteristic;

obtain, from a remote data source responsive to receiving the property classification request, an aerial image of a geographic region including the property;

extract from one or more of a plurality of features from the aerial image corresponding to each property characteristic of the at least one property characteristic, wherein the extracted features include pixel groupings representing the respective property characteristic;

for each of the at least one property characteristic,

determine, from the extracted features for the respective property characteristic, a respective property characteristic classification, wherein determining the respective property characteristic classification includes applying the pixel groupings for the respective property characteristic to a first machine learning classifier trained to identify property characteristics from a set of pixel groupings, and

determine, based on the respective identified property characteristic and the respective extracted features, a respective condition classification for the respective property characteristic, wherein identifying the respective condition classification includes applying the respective pixel groupings for the respective property characteristic to a second machine learning classifier trained to identify property characteristic conditions from a set of pixel groupings; and

determine, in real-time responsive to receiving the property classification request and using the property characteristic classification of each property characteristic and the condition classification of each property characteristic, at least one risk estimate representing risk of damage due to disaster.

16. The non-transitory computer readable medium of claim 15, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to, prior to obtaining the aerial image of the geographic region, determine, based upon the at least one property characteristic, a preferred image type corresponding to each property characteristic of the at least one property characteristic.

17. The non-transitory computer readable medium of claim 16, wherein:

the at least one property characteristic comprises two or more property characteristics; and

the instructions, when executed by the processing circuitry, cause the processing circuitry to, responsive to determining the preferred image type corresponding to a first property characteristic of the at least one property characteristic is a terrestrial image, obtain a terrestrial image of the geographic region including the property.

18. The non-transitory computer readable medium of claim 15, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to access one or more known property characteristics, wherein determining the at least one risk estimate comprises determining the at least one risk estimate further based on the one or more known property characteristics.

19. The non-transitory computer readable medium of claim 18, wherein the one or more known property characteristics comprise at least one of a property age, a property elevation, a property slope, a year built, a year renovated, and a building height.

20. The non-transitory computer readable medium of claim 15, wherein:

receiving identification of the property and the at least one property characteristic comprises receiving, via a network from a remote computing device, the identification of the property; and

the instructions, when executed by the processing circuitry, cause the processing circuitry to provide, via the network to a graphical user interface at the remote computing device in real-time responsive to receiving the at least one property characteristic, the at least one risk estimate.