Technologies for using image data analysis to assess and classify hail damage

A system for analyzing image data to automatically assess hail damage to a property, comprising:

• a memory configured to store non-transitory computer executable instructions;
• and
• a processor interfacing with the memory, and configured to execute the non-transitory computer executable instructions to cause the processor to:
• access digital image data depicting a roof of the property;
• segment the digital image data into a set of digital images depicting a respective set of portions of the roof of the property;
• identify, using a first trained model, a set of regions of potential hail damage from the set of digital images, the first trained model including a convolutional neural network (CNN) configured to:
• classify first individual pixels in a digital image of the set of digital images as indicating potential hail damage, and a remainder of individual pixels in the digital image as indicating an absence of hail damage, and
• generate an output set of images, the output set of images including a binary image depicting:
• the first individual pixels indicating potential hail damage with a first color, and
• the remainder of individual pixels with a second color different from the first color;
• extract a set of features from individual regions of the set of regions of potential hail damage, the set of features comprising a set of texture features indicating at least one of contrast, homogeneity, and entropy;
• and
• analyze, using a second trained model, the set of features to generate a set of outputs indicating a presence of hail damage in the output set of images, the second trained model including a classification model configured to classify the set of features from the individual regions as depicting hail damage.

A non-transitory computer-readable storage medium configured to store instructions, the instructions when executed by a processor causing the processor to perform operations comprising:

• accessing digital image data depicting a roof of a property;
• segmenting the digital image data into a set of digital images depicting a respective set of portions of the roof of the property;
• identifying, using a first trained model, a set of regions of potential hail damage from the set of digital images, the first trained model including a convolutional neural network (CNN) configured to:
• classify first individual pixels in a digital image of the set of digital images as indicating potential hail damage, and a remainder of individual pixels in the digital image as indicating an absence of hail damage, and
• generate an output set of images, the output set of images including a binary image depicting:
• the first individual pixels indicating potential hail damage with a first color, and
• the remainder of individual pixels with a second color different from the first color;
• extracting a set of features from individual regions of the set of regions of potential hail damage, the set of features comprising a set of texture features indicating at least one of contrast, homogeneity, and entropy;
• and
• analyzing, using a second trained model, the set of features to generate a set of outputs indicating a presence of hail damage in the output set of images, the second trained model including a classification model configured to classify the set of features from the individual regions as depicting hail damage.