Damage prediction system using artificial intelligence
View Patent ↗A damage prediction system that uses hazard data and/or aerial images to predict future damage and/or estimate existing damage to a structure is described herein. For example, the damage prediction system may use forecasted hazard data to predict future damage or use actual hazard data to estimate existing damage. The damage prediction system may obtain hazard data in which structures were or will be impacted by a hazard. The damage prediction system can then generate a flight plan that causes an aerial vehicle to fly over the impacted parcels and capture images. The damage prediction system can use artificial intelligence to process the images for the purpose of identifying potential damage. The damage prediction system can also use a hazard model, the hazard data, and structure characteristics to generate a damage score. The damage prediction system can then use the processed images and/or damage score to generate a virtual claim.
1. A system for estimating damage caused by a first type of hazard, the system comprising:
memory storing computer-executable instructions; and
a processor, wherein the computer-executable instructions, when executed by the processor, cause the processor to at least:
train a first artificial intelligence model to identify structural damage using a first set of images annotated to indicate damage caused by the first type of hazard and not a second type of hazard;
train a second artificial intelligence model to identify structural damage using a second set of images annotated to indicate damage caused by the second type of hazard and not the first type of hazard;
determine that the first type of hazard impacted a geographic location in which an object is located;
in response to the determination that the first type of hazard impacted the geographic location, retrieve the trained first artificial intelligence model and apply an image of the object captured after the first type of hazard occurred as an input to the trained first artificial intelligence model; and
output a second image that is annotated to identify structural damage to the object based on application of the image as the input to the trained artificial intelligence model.
2. The system of claim 1 , wherein the image comprises one or more pixels, and wherein the second image comprises a modification to one attribute of one or more pixels of the image.
3. The system of claim 1 , wherein the second image comprises text that indicates that one or more pixels is associated with damage to the object.
4. The system of claim 1 , wherein the computer-executable instructions, when executed, further cause the processor to predict damage to a parcel on which the object resides.
5. The system of claim 1 , wherein the trained first artificial intelligence model is associated with the first type of hazard.
6. The system of claim 1 , wherein the image is obtained from one of an aerial vehicle or a social network.
7. The system of claim 1 , wherein the first type of hazard comprises at least one of hail, wind, flood, storm surge, lightning strike, tornado, hurricane, earthquake, or wildfire.
8. A computer-implemented method of estimating damage caused by a first type of hazard, the computer-implemented method comprising:
training a first artificial intelligence model to identify structural damage using a first set of images annotated to indicate damage caused by the first type of hazard and not a second type of hazard;
training a second artificial intelligence model to identify structural damage using a second set of images annotated to indicate damage caused by the second type of hazard and not the first type of hazard;
retrieving the trained first artificial intelligence model and applying an image of an object captured after the first type of hazard occurred at a geographic location of the object as an input to the trained first artificial intelligence model; and
outputting a second image that is annotated to identify structural damage to the object based on application of the image as the input to the trained artificial intelligence model.
9. The computer-implemented method of claim 8 , wherein the image comprises one or more pixels, and wherein the second image comprises a modification to one attribute of the one or more pixels of the image.
10. The computer-implemented method of claim 8 , wherein the second image comprises text that indicates that one or more pixels is associated with damage to the object.
11. The computer-implemented method of claim 8 , further comprising predicting damage to a parcel on which the object resides.
12. The computer-implemented method of claim 8 , wherein the trained artificial intelligence model is associated with the first type of hazard.
13. The computer-implemented method of claim 8 , further comprising determining that the first type of hazard impacted the geographic location in which the object is located.
14. The computer-implemented method of claim 8 , wherein the first type of hazard comprises at least one of hail, wind, flood, storm surge, lightning strike, tornado, hurricane, earthquake, or wildfire.
15. Non-transitory, computer-readable storage media comprising computer-executable instructions for estimating structural damage caused by a first type of hazard using artificial intelligence, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to:
train an artificial intelligence model to identify structural damage using a first set of images annotated to indicate damage caused by the first type of hazard and not a second type of hazard;
train a second artificial intelligence model to identify structural damage using a second set of images annotated to indicate damage caused by the second type of hazard and not the first type of hazard;
retrieve the trained first artificial intelligence model and apply an image of an object captured after the first type of hazard impacted a location of the object as an input to the trained artificial intelligence model; and
output a second image that is annotated to identify structural damage to the object based on application of the image as an input to the trained first artificial intelligence model.
16. The non-transitory, computer-readable storage media of claim 15 , wherein the image comprises one or more pixels, and wherein the second image comprises a modification to one attribute of the one or more pixels of the image.
17. The non-transitory, computer-readable storage media of claim 15 , wherein the second image comprises text that indicates that one or more pixels is associated with damage to the object.
18. The non-transitory, computer-readable storage media of claim 15 , wherein the computer-executable instructions, when executed, further cause the computer system to predict damage to a parcel on which the object resides.
19. The non-transitory, computer-readable storage media of claim 15 , wherein the trained first artificial intelligence model is associated with the first type of hazard.
20. The non-transitory, computer-readable storage media of claim 15 , wherein the image is obtained from one of an aerial vehicle or a social network.