System and methods for processing imaging data to analyze vegetation

A system for analyzing vegetation, the system comprising one or more processors in communication with one or more data sources and a machine learning model, and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

• receiving, by the one or more processors, real-time data associated with a property of a user from one or more sensors, wherein the data includes one or more images or LiDAR data for the property;
• identifying, by the one or more processors, one or more relevant features associated with the property by applying one or more image analysis techniques to the real-time data, wherein the one or more relevant features include (i) a structural condition change of the property, (ii) a vegetation attribute change of vegetation surrounding the property, or (iii) a weather condition change;
• inputting, by the one or more processors, the one or more relevant features into the machine learning model, wherein the machine learning model is a trained machine learning model that retrieves and processes historical data to learn one or more patterns and trends indicative of one or more hazards to the property;
• generating, by the one or more processors utilizing the trained machine learning model, a hazard prediction corresponding to the property, wherein the trained machine learning model is configured to associate the one or more relevant features with the one or more learned patterns and trends;
• generating, by the one or more processors, one or more recommended actions based upon the hazard prediction for reducing at least one of the one or more hazards;
• presenting, by the one or more processors, the one or more recommended actions in a user interface of a device associated with the user, wherein the one or more recommended actions are arranged based upon a hazard score indicating a probability of occurrence of the one or more hazards damaging the property;
• and
• determining, by the one or more processors, a completion of the one or more recommended actions based upon real-time response data associated with the one or more recommended actions.

A non-transitory computer readable medium for analyzing vegetation, the non-transitory computer readable medium storing instructions which, when executed by one or more processors of a computing system in communication with one or more data sources and a machine learning model, cause the one or more processors to perform operations comprising:

• receiving real-time data associated with a property of a user from one or more sensors, wherein the data includes one or more images or LiDAR data for the property;
• identifying one or more relevant features associated with the property by applying one or more image analysis techniques to the real-time data, wherein the one or more relevant features include (i) a structural condition change of the property, (ii) a vegetation attribute change of vegetation surrounding the property, or (iii) a weather condition change;
• inputting the one or more relevant features into the machine learning model, wherein the machine learning model is a trained machine learning model that retrieves and processes historical data to learn one or more patterns and trends indicative of one or more hazards to the property;
• generating, utilizing the trained machine learning model, a hazard prediction corresponding to the property, wherein the trained machine learning model is configured to associate the one or more relevant features with the one or more learned patterns and trends;
• generating one or more recommended actions based upon the hazard prediction for reducing at least one of the one or more hazards;
• presenting the one or more recommended actions in a user interface of a device associated with the user, wherein the one or more recommended actions are arranged based upon a hazard score indicating a probability of occurrence of the one or more hazards damaging the property;
• and
• determining a completion of the one or more recommended actions based upon real-time response data associated with the one or more recommended actions.