Platform, systems, and methods for identifying characteristics and conditions of property features through imagery analysis

1. A system for automatically assessing property condition, the system comprising:

a non-transitory computer-readable medium storing a plurality of machine learning classifiers, each machine learning classifier trained to identify at least one property attribute or at least one property condition;

a data store comprising property information of a plurality of properties and a plurality of images of the plurality of properties; and

processing circuitry configured to perform operations comprising

accessing, from the data store, one or more aerial images of a property,

identifying, within at least a first aerial image of the one or more aerial images, a footprint of a structure on the property,

applying a first one or more machine learning classifiers of the plurality of machine learning classifiers to at least one of the one or more aerial images to determine a plurality of attributes of the structure, the plurality of attributes comprising a particular roof material of a plurality of roof materials and a particular roof shape of a plurality of roof shapes,

applying a second one or more machine learning classifiers of the plurality of machine learning classifiers to at least one of the one or more aerial images to determine a roof condition of a roof of the structure, wherein

the second one or more machine learning classifiers are trained to, as part of determining a roof condition of the structure, identify one or more types of problems with the roof, and

providing, to a remote computing device via a network, at least a portion of at least one of the one or more aerial images of the property, attribute information corresponding to the plurality of attributes, and condition information corresponding to the roof condition, wherein the condition information comprises identification of one or more problems with the roof, whenever the second one or more machine learning classifiers identifies at least one of the one or more types of problems with the roof.

2. The system of claim 1 , wherein the operations further comprise comparing the roof condition to a historic roof condition.

3. The system of claim 1 , wherein the operations further comprise applying a third one or more machine learning classifiers of the plurality of machine learning classifiers to at least one of the one or more aerial images to determine a plurality of property attributes of one or more features in a vicinity of the structure, the one or more features comprising one or more of a pool, a natural feature, or a tennis court.

4. The system of claim 3 , wherein determining the plurality of property attributes comprises determining at least one of a size or a type of the pool.

5. The system of claim 3 , wherein the operations further comprise determining a distance between the structure and each feature of at least one feature of the one or more features.

6. The system of claim 1 , wherein the one or more types of problems comprise structural damage of one or more roof features.

7. The system of claim 6 , wherein the one or more roof features comprise at least one of a roof covering, a skylight, or a chimney.

8. The system of claim 1 , wherein the operations further comprise obtaining geographic coordinates of the property, wherein at least one of the one or more aerial images is accessed using the geographic coordinates.

9. The system of claim 1 , wherein the operations further comprise determining a numeric condition rating qualifying a relative condition of the roof, wherein the condition information comprises the numeric condition rating.

10. The system of claim 1 , wherein providing the at least the portion of at least one of the one or more aerial images of the property, the attribute information corresponding to the plurality of attributes, and the condition information corresponding to the roof condition comprises presenting, at a display of the remote computing device for review by an end user, a graphical user interface comprising the portion of the at least one of the one or more aerial images, the attribute information, and the condition information.

11. The system of claim 1 , wherein at least a portion of the plurality of images are orthophoto images.

12. A method for automatically assessing property condition, the method comprising:

obtaining geographic coordinates and/or an address corresponding to a property;

retrieving, from at least one database, one or more aerial images of the property;

by processing circuitry, identifying, within at least a first aerial image of the one or more aerial images, a footprint of a building on the property;

applying, by the processing circuitry, a first one or more machine learning models to at least one of the one or more aerial images to classify a plurality of characteristics of the building, the plurality of characteristics comprising a particular roof material of a plurality of roof materials and a particular roof shape of a plurality of roof shapes;

applying, by the processing circuitry, a second one or more machine learning models to at least one of the one or more aerial images to determine a roof condition of a roof of the building, wherein

the second one or more machine learning models are trained to, as part of determining a roof condition of the building, identify one or more types of problems with the roof, and

in real-time responsive to obtaining the geographic coordinates and/or the address, providing, to a remote computing device via a network, at least a portion of at least one of the one or more aerial images of the property, characteristics information corresponding to the plurality of characteristics, and condition information corresponding to the roof condition, wherein

the condition information comprises identification of one or more problems with the roof, whenever the second one or more machine learning models identifies at least one of the one or more types of problems with the roof.

13. The method of claim 12 , wherein the geographic coordinates and/or the address are provided by a user of the remote computing device in relation to a risk analysis request.

14. The method of claim 13 , further comprising determining an amount of risk exposure of the property due at least in part to the roof condition.

15. The method of claim 14 , wherein determining the amount of risk exposure comprises applying a condition classification to the property.

16. The method of claim 12 , wherein the at least one database further comprises property information of a plurality of properties including the property.

17. The method of claim 12 , wherein retrieving the one or more aerial images comprises accessing the one or more aerial images from a remote location via a network.

18. The method of claim 12 , further comprising applying, by the processing circuitry, a third one or more machine learning models to at least one of the one or more aerial images to determine a plurality of property characteristics of one or more additional structures on the property.

19. The method of claim 18 , wherein the one or more additional structures comprise at least one of a pool or a tennis court.

20. The method of claim 12 , wherein providing the at least the portion of at least one of the one or more aerial images of the property, the characteristics information corresponding to the plurality of characteristics, and the condition information corresponding to the roof condition comprises presenting, at a display of the remote computing device for review by an end user, a graphical user interface comprising the portion of the at least one of the one or more aerial images, the characteristics information, and the condition information.