Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically assessing damage vulnerability of a property include accessing digital images of a property parcel having a first structure thereon, classifying features visible in the images, including at least one feature of the first structure and at least one feature present in a neighborhood of the property parcel, to determine at least one of characteristic of each feature, determining a spatial relationship between a first structure and each manmade and/or natural feature represented by the classified features, and applying a property loss risk profile, based at least in part on the determined characteristics and the determined spatial relationships, to calculate a risk estimate for the first structure under at least one risk scenario.

Abstract: In an illustrative embodiment, methods and systems for automatically assessing damage vulnerability of a property include accessing digital images of a property parcel having a first structure thereon, classifying features visible in the images, including at least one feature of the first structure and at least one feature present in a neighborhood of the property parcel, to determine at least one characteristic of each feature, determining a spatial relationship between a first structure and each manmade and/or natural feature represented by the classified features, and applying a property loss risk profile, based at least in part on the determined characteristics and the determined spatial relationships, to calculate a risk estimate for the first structure under at least one risk scenario.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In one aspect, classifying features and feature conditions of a property represented in digital imagery includes training a characteristic classifier for applying multilayer artificial perception to classifying features represented in a property image into feature classifications, training a condition classifier for applying multilayer artificial perception to classifying a condition of each feature into one of a set of condition qualifiers, accessing digital image(s) of a property, and applying a machine learning analysis model including the characteristic classifier and the condition classifier to the digital image(s) to identify, for each feature represented in the image(s), a feature classification and a condition qualifier.

Abstract: In one aspect, classifying features and feature conditions of a property represented in digital imagery includes training a characteristic classifier for applying multilayer artificial perception to classifying features represented in a property image into feature classifications, training a condition classifier for applying multilayer artificial perception to classifying a condition of each feature into one of a set of condition qualifiers, accessing digital image(s) of a property, and applying a machine learning analysis model including the characteristic classifier and the condition classifier to the digital image(s) to identify, for each feature represented in the image(s), a feature classification and a condition qualifier.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically assessing features of a property location may include applying first machine learning analysis to at least one image to determine a set of characteristics of the property features, and applying second machine learning analysis to the at least one image to classify a condition of each property feature.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a repair condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification. The methods and systems may further include determining, using the property characteristic classification and the condition classification, a risk estimate of damage to the property due to one or more disasters and/or a cost estimate of repair or replacement of the property characteristic.

Abstract: In an illustrative embodiment, methods and systems for automatically categorizing a repair condition of a property characteristic may include obtaining aerial imagery of a geographic region including the property, identifying features of the aerial imagery corresponding to the property characteristic, analyzing the features to determine a property characteristic classification, and analyzing a region of the aerial imagery including the property characteristic to determine a condition classification. The methods and systems may further include determining, using the property characteristic classification and the condition classification, a risk estimate of damage to the property due to one or more disasters and/or a cost estimate of repair or replacement of the property characteristic.

Abstract: In an illustrative embodiment, systems and methods for determining exposure include detecting, based on received data from external entities, an occurrence of a catastrophic event. Responsive to detecting the occurrence, a first matrix is generated indicating features of one or more properties within a vicinity of the catastrophic event, and a second matrix is generated representing characteristics of the catastrophic event. Using the first matrix and the second matrix, a convolution calculation matrix is calculated indicating amounts of correspondence between the first matrix and the second matrix. In real time, in response to detecting the occurrence of the catastrophic event, present risk exposure information to at least one remote computing system associated with at least one property of the plurality of properties, where the risk exposure data relates to the amount of correspondence between the first matrix and the second matrix in reference to the at least one property.

Abstract: Systems and methods of the present technology provide the capability to determine risk exposure of points of interest (e.g., insured locations) based on the occurrence of an event (e.g., a catastrophic event). The systems and methods use two-dimensional convolution and FFT processing to provide quick determinations.

Abstract: Systems and methods of the present technology provide the capability to adjust historical claim frequency and/or claim severity data using a weather score process.

Abstract: A catastrophic hazard protection (CHP) mortgage may be provided on real property or structure(s) on the real property of a owner. The CHP mortgage may be structured to include financial protection in the event that one or more structures of the real property is damaged by a catastrophe such as a hurricane or earthquake. The owner may pay an increased interest rate on the CHP mortgage. If catastrophe damage occurs, the principal amount of the CHP mortgage may be reduced by the lesser of the value of the damage or the principal balance. Alternatively, the owner may receive a payment equivalent to the lesser of the value of the damage or the principal balance. A CHP mortgage may be implemented electronically. Additionally, catastrophic hazard protection may be added onto an existing mortgage.