Systems and methods for utilizing property features from images
A process for locating real estate parcels for a user comprises accessing a library of parceled real estate image data to identify objects and features in a plurality of parcels identified by the user as having a feature of interest. A predictive model is constructed and applied to a geographic region selected by the user to generate a customized output of real estate parcels predicted to have the feature of interest, or, in some implementations, not to have the feature of interest.
1. 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 from a user a selection of first images of first real estate parcels in a first geographic region the first images having pixels, the first images identified by the user as having one or more features or characteristics of interest to the user;
construct a predictive model by analyzing the first real estate image data, including at least the first imagery, corresponding to the parcel selection for the one or more features of interest to the user to determine commonalities of the multiple real estate parcels of the parcel selection, the predictive model comprising machine learning algorithms used in one or more neural networks that develop and store correlations for the first imagery based on one or more of: image spectral information, image texture information, and image contextual details; and
apply the predictive model to second images of second real estate parcels in a second geographic region, the second geographic region selected by the user, the second geographic region different from the first geographic region, to identify particular second real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest, the predictive model created by utilizing artificial neural network machine learning algorithms to:
analyze the first images of the first real estate parcels in the first geographic region on a real estate parcel-by-parcel basis, by identifying and classifying features or characteristics within the one or more of the first images; and
develop correlations for the first images of the first real estate parcels, based on one or more of: image spectral information, image texture information, and image contextual details.
2. The system of claim 1 , wherein the instructions, when executed by the one or more processors, cause the one or more processors to:
output to the user the identified particular second real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest.
3. The system of claim 2 , wherein the output is displayed as a map.
4. The system of claim 2 , wherein the output is displayed as a list of addresses.
5. The system of claim 2 , wherein the output comprises one or more of the second images.
6. The system of claim 1 , wherein one or more of the first images and the second images comprise aerial images.
7. The system of claim 1 , wherein the second geographic region is selected by the user from a map.
8. The system of claim 1 , wherein the first images are selected by the user from a map.
9. The system of claim 1 , wherein one or more of the first images and the second images comprise satellite images.
10. The system of claim 1 , wherein one or more of the first images are captured from a street view orientation.
11. A method, comprising:
receiving, with one or more processors, from a user a selection of first images of first real estate parcels in a first geographic region, the first images having pixels, the first images identified by the user as having one or more features or characteristics of interest to the user;
constructing, with the one or more computer processors, a predictive model by analyzing the first real estate image data, including at least the first imagery,
corresponding to the parcel selection for the one or more features of interest to the user to determine commonalities of the multiple real estate parcels of the parcel selection,
the predictive model comprising machine learning algorithms used in one or more neural networks that develop and store correlations for the first imagery based on one or more of: image spectral information, image texture information, and image contextual details;
applying, with the one or more processors, the predictive model to second images of second real estate parcels in a second geographic region, the second geographic region selected by the user, the second geographic region different from the first geographic region, to identify particular second real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest, the predictive model created by the one or more processors by utilizing artificial neural network machine learning algorithms to:
analyze the first images of the first real estate parcels in the first geographic region on a real estate parcel-by-parcel basis, by identifying and classifying features or characteristics within the one or more of the first images; and
developing develop correlations for the first images of the first real estate parcels, based on one or more of: image spectral information, image texture information, and image contextual details.
12. The method of claim 11 , comprising:
outputting, with the one or more processors, to the user, the identified particular second real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest.
13. The method of claim 12 , wherein outputting comprises displaying the identified particular second real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest as a map.
14. The method of claim 12 , wherein outputting comprises displaying the identified particular second real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest as a list of addresses.
15. The method of claim 12 , wherein the outputting the identified particular second real estate parcels in the second geographic region predicted to have the one or more features or characteristics of interest comprises displaying one or more of the second images.
16. The method of claim 11 , wherein one or more of the first images and the second images comprise aerial images.
17. The method of claim 11 , wherein one or more of the first images and the second images comprise satellite images.
18. The method of claim 11 , wherein the second geographic region is selected by the user from a map.
19. The method of claim 11 , wherein one or more of the first images are captured from a street view orientation.
20. 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 from a user a selection of first images of first real estate parcels in a first geographic region, the first images having pixels, the first images identified by the user as having one or more features or characteristics of interest to the user; and
construct a predictive model by analyzing the first real estate image data, including at least the first imagery, corresponding to the parcel selection for the one or more features of interest to the user to determine commonalities of the multiple real estate parcels of the parcel selection, the predictive model comprising machine learning algorithms used in one or more neural networks that develop and store correlations for the first imagery based on one or more of: image spectral information, image texture information, and image contextual details;
apply the predictive model to second images of second real estate parcels in a second geographic region, the second geographic region selected by the user, the second geographic region different from the first geographic region, to identify particular second real estate parcels in the second geographic region predicted to not have the one or more features or characteristics of interest, the predictive model created by utilizing artificial neural network machine learning algorithms to:
analyze the first images of the first real estate parcels in the first geographic region on a real estate parcel-by-parcel basis, by identifying and classifying features or characteristics within the one or more of the first images; and
develop correlations for the first images of the first real estate parcels, based on one or more of: image spectral information, image texture information, and image contextual details.