System and method for home address detection in a geographic region

A method, comprising:

• receiving input of a geographical area of interest on a graphical user interface;
• retrieving home addresses within the geographical area of interest from a storage of home addresses;
• retrieving and storing raw street level images of houses for each of the retrieved addresses;
• cropping the raw street level images to remove portions of adjacent houses in the images;
• storing the cropped images;
• providing the stored cropped images to a Machine Learned Model trained with a training set of street level images of houses with one or more exterior features labeled;
• identifying the home exterior features of interest in the cropped images by way of the Machine Learned Model;
• converting pixels of the identified exterior features into area dimensions;
• and
• quantifying and outputting information for each of the identified exterior features in the one or more stored cropped images, wherein the outputting comprises information representing characteristics of each image in the one or more of the cropped images, the information having text information comprising:
• a unique alphanumeric identifier;
• street address;
• geographic location;
• date of image acquisition;
• and
• image URL.

A method, comprising:

• receiving input of a geographical area of interest on a graphical user interface;
• retrieving home addresses within the geographical area of interest from a storage of home addresses;
• retrieving and storing raw street level images of houses for each of the retrieved addresses;
• cropping the raw street level images to remove portions of adjacent houses in the images;
• storing the cropped images;
• providing the stored cropped images to a Machine Learned Model trained with a training set of street level images of houses with one or more exterior features labeled;
• identifying the home exterior features of interest in the cropped images by way of the Machine Learned Model;
• converting pixels of the identified exterior features into area dimensions;
• and
• quantifying and outputting information for each of the identified exterior features in the one or more stored cropped images, wherein the outputted information for each one of the one or more street level images is the identification of exterior features for each one of the one or more street level images are outputted as a bar chart.

A method, comprising:

• receiving input of a geographical area of interest on a graphical user interface;
• retrieving home addresses within the geographical area of interest from a storage of home addresses;
• retrieving and storing raw street level images of houses for each of the retrieved addresses;
• cropping the raw street level images to remove portions of adjacent houses in the images;
• storing the cropped images;
• providing the stored cropped images to a Machine Learned Model trained with a training set of street level images of houses with one or more exterior features labeled;
• identifying the home exterior features of interest in the cropped images by way of the Machine Learned Model;
• converting pixels of the identified exterior features into area dimensions;
• and
• quantifying and outputting information for each of the identified exterior features in the one or more stored cropped images, wherein the outputted information for each one of the one or more street level images is the identification of exterior features for each one of the one or more street level images are outputted as a heat map.

A method, comprising:

• receiving input of a geographical area of interest on a graphical user interface;
• retrieving home addresses within the geographical area of interest from a storage of home addresses;
• retrieving and storing raw street level images of houses for each of the retrieved addresses;
• cropping the raw street level images to remove portions of adjacent houses in the images;
• storing the cropped images;
• providing the stored cropped images to a Machine Learned Model trained with a training set of street level images of houses with one or more exterior features labeled;
• identifying the home exterior features of interest in the cropped images by way of the Machine Learned Model;
• converting pixels of the identified exterior features into area dimensions;
• and
• quantifying and outputting information for each of the identified exterior features in the one or more stored cropped images, wherein the outputted information for each one of the one or more street level images is the identification of exterior features for each one of the one or more street level images are outputted and converted to a percentage of total counts or total area for all of the identified exterior features and outputted as a pie chart.