Systems and methods for utilizing property features from images

1. A method, comprising:

collecting images of a first geographic region, the images having pixels;

applying real estate parcel data to the images of the first geographic region;

creating a predictive model by analyzing the images of the first geographic region on a parcel by parcel basis, by identifying and classifying objects within each parcel; and

generating a library of parceled real estate image data within a database utilizing the predictive model, the parceled real estate image data including the identified and classified objects and the images.

2. The method of claim 1 , wherein the objects are externally discernable and shared in a plurality of real estate parcels that are identified by a user as having one or more features or characteristics of interest.

3. The method of claim 2 , wherein creating the predictive model comprises utilizing artificial neural network machine learning algorithms.

4. The method of claim 2 , wherein creating the predictive model comprises utilizing artificial neural network machine learning algorithms to develop correlations for the plurality of real estate parcels identified by the user, based on one or more of: image spectral information, texture information, and contextual details.

5. The method of claim 2 , comprising:

applying the predictive model to images of a second geographic region selected by the user, to identify real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest, the second geographic region different from the first geographic region.

6. The method of claim 2 , wherein the one or more features or characteristics of interest are visually discernible within the images.

7. The method of claim 1 , wherein the images are captured with a sensor located in one or more of a street view orientation, a satellite, or an aircraft.

8. The method of claim 1 , wherein the parceled real estate image data includes physical location of objects in a property depicted in the image and contextual relationships between objects depicted in the image.

9. The method of claim 1 , wherein the real estate parcel data includes individual land owner borders.

10. The method of claim 2 , comprising:

receiving information from a user indicative of a feel of the user toward parcels in the plurality of real estate parcels identified by the user; and

relating the feel to the identified objects within the parcels.

11. A system, comprising:

a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

receive images of a geographic area;

identify pixels of the images corresponding to real estate parcels by applying geographic parcel data comprising geographic boundaries of real estate parcels to the images of the geographic area;

create a customized predictive model by identifying and classifying objects within the real estate parcels by analyzing the pixels of the images corresponding to the real estate parcels; and

generate a library of parceled real estate image data, the parceled real estate image data including the identified and classified objects and the images.

12. The system of claim 11 , wherein creating the customized predictive model comprises:

analyzing the pixels of the images corresponding to a selection from a user of multiple real estate parcels from a first section of the geographic area, the selection of multiple real estate parcels having one or more visually discernable user-desired characteristics associated with the real estate parcels, by utilizing machine learning algorithms in one or more neural networks to determine correlations for the selection of multiple real estate parcels based on one or more of: image spectral information, image texture information, image contextual details, and patterns in the pixels.

13. The system of claim 12 , wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

receive, from the user, a selection of a second section of the geographic area;

identify one or more real estate parcels in the second section predicted by the customized predictive model to have the one or more visually discernable user-desired characteristics by:

analyzing pixels of the images corresponding to real estate parcels in the second section of the geographic area, from the library of real estate image data, with the customized predictive model comprising the machine learning algorithms in the one or more neural networks; and

generate a customized output of the identified real estate parcels predicted to have the one or more visually discernable user-desired characteristics.

14. The system of claim 13 , wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

generate a second customized output of the identified real estate parcels predicted to not have the one or more visually discernable user-desired characteristics.

15. The system of claim 13 , wherein the customized output of the identified real estate parcels is displayed as a map.

16. The system of claim 13 , wherein the customized output of the identified real estate parcels is displayed as a list of addresses.

17. The system of claim 12 , wherein receiving from the user the real estate parcel selection includes receiving physical addresses of the multiple real estate parcels.

18. The system of claim 11 , wherein the instructions, when executed by the one or more processors, cause the one or more processors to:

store the library of parceled real estate image data in a database.

19. The system of claim 11 , wherein the images comprise aerial images.

20. The system of claim 11 , wherein the images comprise satellite images.