Patent Publication Number: US-2022215744-A1

Title: Wildfire defender

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 17/192,120 filed Mar. 4, 2021, which is a continuation of U.S. patent application Ser. No. 16/585,565 filed Sep. 27, 2019 and issued as U.S. Pat. No. 10,964,201, the entire contents of which are specifically incorporated by reference herein. 
    
    
     BACKGROUND 
     Property in fire prone areas can have different risks of impact by a fire, such as a wildfire. There are various regulations and guidelines that define fire safety codes for establishing and maintaining a reduced impact of spreading a wildfire between properties. Fire safety codes may vary between jurisdictions and can change over time. Further, a property may initially comply with separation distance requirements between trees and dwellings as defined in fire safety codes, but over time, tree growth may result in reduced separation distances. Additionally, new vegetation may sprout and grow in previously open spaces that results in reduced separation distances. As compliance with fire safety codes changes over time, the risks of wildfire damage can change locally at a particular property and across neighboring properties. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The features and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG. 1  depicts a block diagram of a system according to some embodiments of the present invention; 
         FIG. 2  depicts a block diagram of a system according to some embodiments of the present invention; 
         FIG. 3  depicts a data collection pattern according to some embodiments of the present invention; 
         FIG. 4  depicts a simplified view of various objects that may be observed using the data collection pattern of  FIG. 3  according to some embodiments of the present invention; 
         FIG. 5  depicts examples of three-dimensional models that can be constructed from multiple datasets according to some embodiments of the present invention; 
         FIG. 6  depicts a data merging process to form merged model data according to some embodiments of the present invention; 
         FIG. 7  depicts a training and prediction process according to some embodiments of the present invention; 
         FIG. 8  depicts a wildfire risk map according to some embodiments of the present invention; 
         FIG. 9  depicts an example of geographic features that can impact a fire path spread pattern according to some embodiments of the present invention; 
         FIG. 10  depicts a remote user interface example according to some embodiments of the present invention; 
         FIG. 11  depicts a user interface according to some embodiments of the present invention; 
         FIG. 12  depicts a process using wildfire risk analysis for rating and quoting according to some embodiments of the present invention; 
         FIGS. 13A and 13B  depict a process flow according to some embodiments of the present invention; 
         FIG. 14  depicts a process flow according to some embodiments of the present invention; 
         FIG. 15  depicts a process flow according to some embodiments of the present invention; 
         FIG. 16  depicts a process flow according to some embodiments of the present invention; and 
         FIG. 17  depicts a process flow according to some embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     According to an embodiment, a system for wildfire risk analysis using image processing and supplemental data is provided. The system may be used for various practical applications of extracting information from image data in combination with one or more data sources. By using image data and accessing one or more related data sources, many wildfire risk factors can be determined for a geographic area. The wildfire risk data can be used to predict wildfire spread patterns, predictively alert parties in a likely fire spread path, and/or alert responders. Some types of data can be discovered from a single viewing perspective, such as an overhead view from aerial imagery data, using artificial intelligence/machine learning to locate features of interest in a large volume of data. Other types of data can be discovered when multiple datasets are merged or accessed in parallel. For example, using datasets from multiple viewing perspectives can enable construction of partial or complete three-dimensional models that can be further analyzed to discover features of interest using artificial intelligence/machine learning that may not be readily discernable from analyzing the datasets in isolation. A partial three-dimensional model can incorporate features or otherwise correlate features in three-dimensional space without creating a full rendering of objects in three-dimensional space. For instance, a planimetric image can be formed in one viewing perspective, and one or more other images having a different viewing perspective can be accessed to correlate data associated with a feature of interest from the planimetric image with the one or more other viewing perspectives to observe portions of the feature of interest in three-dimensional space. Some features of interest may be observed using only a single viewing perspective of a two-dimensional image. For example, a planimetric image can show a horizontal portion of features compiled into map features through a photogrammetric process with accurate horizontal distances between features, such as paved surfaces, building footprints, waterbodies, vegetation, and various manmade features. 
     Further, a group of machine-learning models can be developed that looks for specific features, groups of features, and various characteristics associated with properties viewed at a wider scale (e.g., a neighborhood) and a detailed lower-level scale, such as roofing or siding material type. The use of supplemental property data can enhance the visual data, such as identifying features that are not directly visible in the image data (e.g., property boundaries). The height and relative health of vegetation can be determined using the imagery, which can then be used to determine a predicted level of combustibility of the vegetation along with other factors. Ground covering, relative moisture, heat retention, natural fire barriers (e.g., bodies of rock or water), and other such features may be identified and used in wildfire risk analysis, as further described herein. 
     In embodiments, network performance may be enhanced by locally caching portions of datasets and analysis previously performed, for example, where real-time analysis is not needed. As learning is performed for a particular geographic area, records can be tagged with date/time stamps for comparison against source data in future iterations. For instance, if multiple users are accessing an analysis tool that performs machine learning for a particular geographic area, a copy of the analysis results can be stored within an enterprise storage system to prevent repetitive application of machine learning and data transfer requests across a network by using the stored copies of analysis results and/or datasets received from a third-party source. When a new request for analysis is made, the enterprise storage system can be checked first to see if a copy of the desired information is already locally available. Further, before requesting a new transfer of data from a remote data source, a date-time of last refresh can be checked at the remote data source to verify whether the desired data has been updated such that it no longer aligns with a copy previously acquired and stored within the enterprise storage system. If new data exists, then associated datasets can be transferred to the enterprise storage system to apply machine-learning processes on the updated data. 
     Turning now to  FIG. 1 , a system  100  is depicted upon which wildfire analysis may be implemented. The system  100  can include an enterprise network zone  101  including a data processing server  102  coupled to a gateway  104  operable to establish communication with one or more user systems  106 , one or more data storage servers  110 , and/or other devices (not depicted) through an enterprise network  108 . The gateway  104  may also establish communication to an external network  114 , for instance, through a firewall  112 , to send data to and receive data from a plurality of third-party servers  116  in an external network zone  115 . The third-party servers  116  can each execute one or more third-party services  118 . Examples of third-party services  118  can include, for instance, data collection, processing and analytics services that operate on large volumes of data and are implemented by third parties, such as vendors, advisors, brokers, and the like. For instance, third-party services  118  can provide aerial imagery data  119 , property data  121 , and other such data. Further, the third-party services  118  can generate or manage various types of maps  123 . Maps  123  can be geographic feature maps identifying topography of land and bodies of water, and/or weather maps of past, present, and future predictions (e.g., forecasts), for example. In some embodiments, one or more wildfire risk maps generated by the data processing server  102  can be stored in the maps  123  for use by various third-party services  118  or remote user systems  125  operable to execute a remote user interface  127 . Although maps  123  are depicted as a single entity, it will be understood that the maps  123  can be distributed over multiple third-party servers  116 , for instance, where geographic feature maps, weather maps, and wildfire risk maps are separately managed. 
     In embodiments, the enterprise network zone  101  can include a plurality of networked resources that may be distributed over multiple locations, where the networked resources are access-controlled by an enterprise. The external network zone  115  may link to networked resources that are outside of enterprise control and may be distributed over a wide geographic area. 
     In the example of  FIG. 1 , the data processing server  102  is operatively coupled to a data cache  120  that provides short-term data buffering of datasets  122  and location specific data  124  extracted from the third-party services  118  and further processed using artificial intelligence (AI) models  126 . A process controller  128  can execute on the data processing server  102  to manage data acquisition, use of AI models  126 , storage to the data cache  120 , and interface with other components of the system  100 . The AI models  126  can be trained to detect features of interest in the datasets  122  and location specific data  124 . Further, the AI models  126  can apply multiple levels of models to discover patterns between multiple datasets  122  and derived characteristics. The AI models  126  can be applied across various file types and data structures, such as images, text, and/or other data formats. The AI models  126  can apply machine-learning algorithms to identify various features, such as buildings, objects, and other structures, along with characteristics of the buildings (e.g., footprint size, number of levels, roofing type, siding type, exterior condition, and other such characteristics). Features, such as property boundaries, can be extracted from the location specific data  124  to summarize features of specific properties, and wider-scale AI models  126  can be applied to discover neighborhood or regional patterns associated with a targeted geographic location. The AI models  126  can learn new types of patterns, variations, and/or rules as new instances of datasets  122  and location specific data  124  are encountered. 
     Examples of algorithms that may be applied to train the AI models  126  can include one or more of: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. For instance, labeled training data can be provided to train the AI models  126  to find model parameters that assist in detecting unlabeled data in the datasets. Linear regression and linear classifiers can be used in some embodiments. Other embodiments may use decision trees, k-means, principal component analysis, neural networks, and/or other known machine-learning algorithms. Further, the AI models  126  may use a combination of machine-learning techniques that can differ depending on whether the dataset includes text, image data, and/or layered data. Layered data can refer to multiple types of data associated with the same location, such as visible spectrum image data, infrared image data, depth data, and the like. For example, supervised learning with entity extraction can be used to learn text values, while generative adversarial networks can be used for image learning. 
     A user application  132  executed on one or more of the user systems  106  may provide an interface to select locations for analysis. The user application  132  can interface with the process controller  128  to determine whether characteristics associated with a targeted location have recently been analyzed by the AI models  126  with results available. For instance, when a targeted location is not captured in the datasets  122  and location specific data  124  in data cache  120 , the process controller  128  can access the aerial imagery data  119  to extract one or more datasets and access the property data  121  for analysis by the AI models  126 . Values of the aerial imagery data  119  may be stored temporarily in the datasets  122  and values of the property data  121  may be stored temporarily in the location specific data  124 . The process controller  128  may perform preprocessing and postprocessing on the datasets  122  and location specific data  124  prior to analysis by the AI models  126  and after results are determined by the AI models  126 . The process controller  128  can store results of the AI models  126  in data storage to support longer-term trending analysis. If the user application  132  requests a location analysis for a location that already has associated data in the data cache  120 , the process controller  128  may check a date/time stamp associated with the datasets  122  and location specific data  124  to determine whether more recent data is available in the aerial imagery data  119  or property data  121 . If more recent data is available, then the more recent data can be transferred to the data cache  120  and updated analysis performed using the AI models  126 . If the data in the data cache  120  is still fresh, then the results of previous analysis can be provided back to the user application  132  to increase responsiveness and reduce network traffic between the enterprise network zone  101  and the external network zone  115 . Results of data processing using the AI models  126  can be provided to other models (not depicted) as part of a model hierarchy, such as risk models, loss models, and the like. Subsequent analysis and actions can be performed locally in the enterprise network zone  101 , remotely in the external network zone  115 , or a combination thereof. 
     In some embodiments, the user application  132  or another administrative application (not depicted) can configure one or more aspects of the AI models  126 , for instance, to constrain features of interest for the AI models  126  to analyze. As an example, the user application  132  can operate in a two-dimensional analysis mode where image analysis is performed from a single viewing perspective to enhance responsiveness or a three-dimensional analysis mode where data from multiple viewing perspectives is combined to detect features in surfaces and contours that may not otherwise be discernable from a single viewing perspective. Further, the user application  132  may support a batch processing mode where a list of addresses is passed to the process controller  128  for analysis. The process controller  128  can create a plurality of records associated with batch processing for a plurality of properties and generate a sequence of processing requests based on the records. The processing requests can include a scoring computation, for instance, to estimate a condition, age, value, combustion risk, or other parameter associated with the identified features. In the case of preparing a quote for an insurance policy or other purpose, the result of a wildfire risk scoring computation based on comparing contents of a record to one or more scoring thresholds can be forwarded with the record to another application and/or user identifier associated with the property. Other processing and uses of the results from AI models  126  are contemplated and further described herein. 
     In the example of  FIG. 1 , each of the data processing server  102 , user systems  106 , data storage servers  110 , third-party servers  116 , and remote user systems  125  can include one or more processors (e.g., a processing device, such as one or more microprocessors, one or more microcontrollers, one or more digital signal processors) that receives instructions (e.g., from memory or like device), executes those instructions, and performs one or more processes defined by those instructions. Instructions may be embodied, for example, in one or more computer programs and/or one or more scripts. In one example, the system  100  executes computer instructions for implementing the exemplary processes described herein. Instructions that implement various process steps can be executed by different elements of the system  100 . Although depicted separately, one or more of the data processing server  102 , user systems  106 , and/or data storage servers  110  can be combined or further subdivided. The system  100  can also include other subsystems (not depicted) that support processes which access and use data generated by the data processing server  102 , user systems  106 , data storage servers  110 , third-party servers  116 , and/or remote user systems  125 . 
     The user systems  106  may each be implemented using a computer executing one or more computer programs for carrying out processes described herein. In one embodiment, the user systems  106  may each be a personal computer (e.g., a laptop, desktop, etc.), a network server-attached terminal (e.g., a thin client operating within a network), or a portable device (e.g., a tablet computer, personal digital assistant, smart phone, etc.). In an embodiment, the user systems  106  are operated by analysts seeking information about properties without having to physically travel to the properties. It will be understood that while only a single instance of the user systems  106  is shown in  FIG. 1 , there may be multiple user systems  106  coupled to the enterprise network  108  in embodiments. Similarly, remote user systems  125  can be used by remotely-deployed analysts or other types of users, such as parties having an interest in the condition of property. The remote user systems  125  can be used by property owners to understand wildfire risks and, in some embodiments, receive real-time alerts of a wildfire event in progress and a predicted arrival time of the wildfire at the property. Further, the remote user systems  125  can be used by first-responders in tracking wildfire spreading patterns and a predicted path of the wildfire, e.g., along routes having higher wildfire risk scores. 
     Each of the data processing server  102 , user systems  106 , data storage servers  110 , third-party servers  116 , and remote user systems  125  can include a local data storage device, such as a memory device. A memory device, also referred to herein as “computer-readable memory” (e.g., non-transitory memory devices as opposed to transmission devices or media), may generally store program instructions, code, and/or modules that, when executed by a processing device, cause a particular machine to function in accordance with one or more embodiments described herein. Aspects of the system  100  of  FIG. 1  can be distributed in other arrangements beyond the example of  FIG. 1 . For instance, the AI models  126  may be trained and/or managed by one or more of the third-party services  118 . 
       FIG. 2  depicts a block diagram of a system  200  according to an embodiment. The system  200  is depicted embodied in a computer  201  in  FIG. 2 . The system  200  is an example of one of the data processing server  102 , user systems  106 , data storage servers  110 , third-party servers  116 , and/or remote user systems  125  of  FIG. 1 . 
     In an exemplary embodiment, in terms of hardware architecture, as shown in  FIG. 2 , the computer  201  includes a processing device  205  and a memory device  210  coupled to a memory controller  215  and an input/output controller  235 . The input/output controller  235  may comprise, for example, one or more buses or other wired or wireless connections, as is known in the art. The input/output controller  235  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the computer  201  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     In an exemplary embodiment, a keyboard  250  and mouse  255  or similar devices can be coupled to the input/output controller  235 . Alternatively, input may be received via a touch-sensitive or motion sensitive interface (not depicted). The computer  201  can further include a display controller  225  coupled to a display  230 . 
     The processing device  205  comprises a hardware device for executing software, particularly software stored in secondary storage  220  or memory device  210 . The processing device  205  may comprise any custom made or commercially available computer processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the computer  201 , a semiconductor-based microprocessor (in the form of a microchip or chip set), a macro-processor, or generally any device for executing instructions. 
     The memory device  210  can include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), flash memory, programmable read only memory (PROM), tape, compact disk read only memory (CD-ROM), flash drive, disk, hard disk drive, diskette, cartridge, cassette or the like, etc.). Moreover, the memory device  210  may incorporate electronic, magnetic, optical, and/or other types of storage media. Accordingly, the memory device  210  is an example of a tangible computer readable storage medium upon which instructions executable by the processing device  205  may be embodied as a computer program product. The memory device  210  can have a distributed architecture, where various components are situated remotely from one another, but can be accessed by one or more instances of the processing device  205 . 
     The instructions in memory device  210  may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. In the example of  FIG. 2 , the instructions in the memory device  210  include a suitable operating system (O/S)  211  and program instructions  216 . The operating system  211  essentially controls the execution of other computer programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. When the computer  201  is in operation, the processing device  205  is configured to execute instructions stored within the memory device  210 , to communicate data to and from the memory device  210 , and to generally control operations of the computer  201  pursuant to the instructions. Examples of program instructions  216  can include instructions to implement the third-party services  118 , AI models  126 , process controller  128 , user application  132 , and/or remote user interface  127  of  FIG. 1 . 
     The computer  201  of  FIG. 2  also includes a network interface  260  that can establish communication channels with one or more other computer systems via one or more network links. The network interface  260  can support wired and/or wireless communication protocols known in the art. For example, when embodied in one of the user systems  106  or remote user systems  125  of  FIG. 1 , the network interface  260  can establish communication channels with at least one of the data processing server  102  or data storage servers  110  via the enterprise network  108  and/or with third-party servers  116  via external network  114 . 
       FIG. 3  depicts a data collection pattern according to some embodiments. The data collection pattern can include capturing of data using a grid pattern  300 . The grid pattern  300  may include a plurality of grid cells  302  that represent a geographic area as observed from a substantially consistent altitude forming a plurality of grid rows  304  and grid columns  306 . A mobile observation platform  305  can travel in a first direction, such as across a grid row  304 , to capture images and/or other types of data from a first viewing perspective  308  in a first dataset  318 . For example, the mobile observation platform  305  can be an aircraft, such as an airplane, helicopter, drone, or the like. The sensing capabilities of the mobile observation platform  305  can include at least one camera configured to capture images or video in a visible spectrum across the grid rows  304 . In some embodiments, the mobile observation platform  305  can capture layered data by capturing images using an infrared camera, a depth camera (e.g., LIDAR), and/or other imaging techniques known in the art. The mobile observation platform  305  can also traverse the same geographic area to capture images and/or other types of data across grid columns  306  of the grid pattern  300  from a second viewing perspective  310  in a second dataset  320 . Data captured across the grid pattern  300  may be postprocessed to stitch the images together to make a larger scale area map that seamlessly links the grid cells  302  together. In some embodiments, the intermediate images of multiple frames captured may also be retained to show changing perspectives as the mobile observation platform  305  travels, which may reveal details that are obstructed when viewed from directly overhead or at an alternate angle of observation. As compared to conventional regional views of aerial imagery, which may be constrained to a resolution of about 50 cm per pixel, the aerial imagery data captured in the first dataset  318  and second dataset  320  may have a resolution of 7.5 cm per pixel, for example, which enables finer details to be observed. Sizing of the grid cells  302  can vary from a pixel-level to multiple meters or kilometers depending on the observation range, altitude during data collection, and zoom level set during data gathering. Although depicted as a uniform distribution of rectangular-shaped grid cells  302  for purposes of illustration, it will be understood that the grid pattern  300  can be a collection of non-uniform shaped polygons and may include partial boundaries. For instance, a coordinate space of the grid pattern  300  can align with property boundaries, which may have any orientation or shape, e.g., as impacted by features of the underlying terrain and other such constraints. Any type of coordinate system or coordinate transformation process may be supported. 
       FIG. 4  depicts a simplified view of various objects that may be observed using the data collection pattern of  FIG. 3  according to embodiments. In  FIG. 4 , a grid pattern  400  includes a different geographic area than that depicted in  FIG. 3 . The simplified example of  FIG. 4  depicts how the mobile observation platform  305  may observe features on the ground from at least two different viewing perspectives, including the first viewing perspective  308  and the second viewing perspective  310  to form grid rows  304  and grid columns  306  of grid cells  302 . Further, the mobile observation platform  305  may include cameras pointing in a direction of travel of the mobile observation platform  305  and in an opposite direction to the direction of travel. Thus, a structure, such as a building  402 , a car  404 , a house  406 , vegetation  408 , and/or other observable features, can be captured on approach, from above, and on egress from the first viewing perspective  308  and the second viewing perspective  310 , such that a complete view is captured in the combination of the first dataset  318  and the second dataset  320 . 
     The high-resolution image data can enable measurements between various features captured in the images and identified through the AI models  126  of  FIG. 1 . For instance, the AI models  126  can be used to determine an estimated distance  410  between houses  406  or other structures on the same property or between properties. Further, the AI models  126  can be used to determine an estimated distance  412  between vegetation  408  (e.g., trees) on the same property and/or determine an estimated distance  414  between vegetation  408  on different properties. As another example, the AI models  126  can be used to determine an estimated distance  416  between vegetation  408  and a house  406  or other structure on a same property or an estimated distance  418  between vegetation  408  and a house  406  or other structure on different properties. The estimated distances can be collected as separation data for further comparison in computing a defensible space adherence score and other such scores. Other distance estimates can also be computed beyond the examples illustrated with respect to  FIG. 4 . The AI models  126  can be trained to identify various objects with different levels of granularity. For example, building material type, building material condition, roof type, roof condition, vegetation type, vegetation condition, and other types of object classification can be performed where tracking such information assists in fire risk determination. Where objects, such as vehicles (e.g., cars  404 ) are considered in the defensible space adherence score and/or fire risk assessment, the AI models  126  may also be trained to distinguish between vehicle types, which may represent a fuel source (e.g., gasoline, diesel, lithium-based battery). 
       FIG. 5  depicts examples of three-dimensional models  500  that can be constructed from multiple datasets according to embodiments. The data processing server  102  of  FIG. 1  can merge data from the first dataset  318  and the second dataset  320  of  FIG. 4  to create various three-dimensional models  502 ,  504 ,  506 ,  508 , which can be partially or fully rendered. For example, a three-dimensional model  502  of building  402  of  FIG. 4  can capture various details, such as a number of floors  511 . A three-dimensional model  504  of personal property, such as car  404  of  FIG. 4 , can enable classifying a type of personal property as fixed or movable, as well as viewing the condition of the personal property for portions that are visible. In the example of the car  404 , it may be difficult to discern certain features if parked in close proximity to other vehicles, structures, or other obstructions. In some instances, positioning of personal property in proximity to structures, such as a house  406  of  FIG. 4 , can temporarily impact a wildfire risk to the house  406  as a source of fuel for a fire to bridge a gap of otherwise open space for the fire to propagate. A three-dimensional model  506  of the house  406  of  FIG. 4  can enable viewing of details, such as a roof  512 , siding  514 , windows/doors  516 , a chimney  518 , an overhang  520 , and other such features, including features in proximity to the house  406 . Details, such as the shape of the roof  512 , state of the chimney  518  (e.g., presence of a chimney cap), vents (e.g., ridge vent in roof  512 , soffits, end vents, etc.), building material type, and building material condition may be observed and tracked. Other details may include identifying a location of an electrical or gas utility connection at a structure. A three-dimensional model  508  of the vegetation  408  of  FIG. 4 , such as a tree, bush, or other plant life, can enable a determination of the approximate height/size, area, type/species, and condition of the vegetation  408 . The three-dimensional model  508  of vegetation  408  can reveal aspects, such as dead spots  510  that may not be discernable in some views. Other such features captured in the first dataset  318  and the second dataset  320  can be modeled as well. In some embodiments, the formation of the three-dimensional models  500  can be performed by another entity, such as the third-party servers  116  of  FIG. 1 . The three-dimensional models  500  may have varying levels of detail, such that data relating to features of interest is more fully defined in the context of three-dimensional space, while other potentially observable features are rendered in two-dimensional space. In some embodiments having sufficient processing and memory bandwidth, the three-dimensional models  500  can be fully rendered rather than partially rendered. As a greater level of detail is desired for a feature of interest, multiple viewing perspectives can be accessed to corelate different views, such as a planimetric image with one or more viewing perspectives to identify related details in three-dimensional space. 
       FIG. 6  depicts a data merging process  600  to form merged model data according to some embodiments. Different types of data with different resolution can be merged by the data processing server  102  of  FIG. 1 . As an example, image data  602  can be accessed from the aerial imagery data  119  of  FIG. 1  and be stored as part of datasets  122  of  FIG. 1 . Location specific data  604  can be accessed from the property data  121  of  FIG. 1  and/or other sources and be stored as part of location specific data  124  of  FIG. 1 . Pixels of image data  602  can represent very small areas (e.g., cm scale), while the location specific data  604  may represent data at a different scale or different units (e.g., coordinate system) to define a property location and boundaries. The data processing server  102  can perform processing to map location information represented in the image data  602  to location information represented in the location specific data  604 . Data merging  608  can link or otherwise combine portions of the image data  602  and location specific data  604  into a combined format that can be understood by the AI models  126  of  FIG. 1  as merged model data  610 . Similarly, where more detailed data is available, such as layered data  606 , a separate data merging  612  can be used to form merged model data  614  that links or combines the location specific data  604  with the layered data  606 . As an example, the layered data  606  can include two or more of a visible light image layer  616 , an infrared image layer  618 , and a depth layer  620 . The visible light image layer  616  can be formatted as pixels using a color space, such as red-green-blue (RGB) pixels, grayscale intensity, or other known image formats. Using a combination of sensors (e.g., a visible-spectrum camera, an infrared camera, and a depth sensor) in parallel to collect data can enable the visible light image layer  616 , the infrared image layer  618 , and the depth layer  620  to be captured at the same time to provide visual data, heat-based data, and height data in a same viewing area and a same time for each data frame of the layered data  606 . The data merging  608 ,  612  can enable inputs having various characteristics and scaling to be normalized or otherwise preprocessed for use by the AI models  126 . With respect to the first dataset  318  from the first viewing perspective  308  and the second dataset  320  from the second viewing perspective  310 , each dataset  318 ,  320  may have separate merged model data  610 ,  614  to perform perspective-specific or simplified analysis. The three-dimensional models  500  can have separate combinations of the merged model data  610 ,  614  formed as merged with location specific data  604 , if desired. It will be understood that any number of data sources can be merged, and in some embodiments, data sources can be processed using the AI models  126  without merging. In some embodiments, the data merging process  600  can be performed by another entity, such as the third-party servers  116  of  FIG. 1 . Although depicted as uniform grids in  FIG. 6 , the location specific data  604  may be formed of irregular polygons. Further, layers  616 ,  618 , and  620  may have different resolutions and/or non-uniformity of distribution depending on the resolution and collection perspective of underlying sensors used to collect data. Embodiments can manage different coordinate spaces or make function calls to existing space transformation services to enable the different coordinate spaces to be correlated. 
       FIG. 7  depicts a training and prediction process  700  according to some embodiments. The training and prediction process  700  can include a training process  702  that analyzes training data  704  to develop trained models  706  as examples of the AI models  126  of  FIG. 1 . The training process  702  can use labeled or unlabeled data in the training data  704  to learn features, such as a building footprint, roof identification, construction material type, various property features, and/or other derived characteristics. The training data  704  can include a set of training images and other data to establish a ground truth for learning coefficients/weights and other such features known in the art of machine learning to develop trained models  706 . The training data  704  can include multiple layers of data, such as visual image data, infrared image data, and depth data to support training across multiple sensor types in parallel. For instance, classification of objects can be defined in terms of a combination of one or more types of images with depth data for three-dimensional analysis. The trained models  706  can include a family of models to identify specific types of features from model data  708 . For example, the trained models  706  can include a building detection model  710  and a vegetation detection model  712 . Other such models and further subdivision of the trained models  706  can be incorporated in various embodiments. The building detection model  710  can predict, for instance, a roof location of a building based on individual pixel data and an aggregation of the individual pixel data. The vegetation detection model  712  can identify vegetation position, a vegetation outline, type, health, and other such features with seasonal adjustments, such as summer condition versus winter condition. Further, the building detection model  710  and vegetation detection model  712  can be tuned to look for specific features, such as identifying a roof overhang of the building based on a three-dimensional model or other data or identifying branches extending in closer proximity to a portion of a building. Distinguishing features, such as overhangs, can improve the accuracy of building footprint determinations of the underlying structure below the roof. Further, vegetation health can change a fire risk along with the presence of leaves/needles and moisture content. 
     In embodiments, the building detection model  710  can be created based on a building footprint dataset, which may be extracted from the property data  121  of  FIG. 1  associated with a geographic area defined in the training data  704 . Building footprints in the training data  704  can overlay image data extracted from the aerial imagery data  119  of  FIG. 1  associated with the geographic area defined in the training data  704 . An expert can view the alignment and make adjustments to fix skewed alignment results as an adjusted training set in the training data  704 . The adjusted training set in the training data  704  can be used for the building detection model  710 . The training process  702  can train the building detection model  710  using machine-learning techniques, such as image segmentation with masking and regions with convolutional neural networks or other such techniques to support building footprint detection based on image data from the aerial imagery data  119 . As another example, the vegetation detection model  712  can start with image data extracted from the aerial imagery data  119  of  FIG. 1  associated with a geographic area defined in the training data  704 . An expert can trace vegetation details, such as tree canopies through a graphical user interface and label the tree canopies. Examples can be selected for many vegetation types and seasonal variations at multiple geographic areas. Image segmentation can be performed on tree canopies rather than trunks or centroids to distinguish between tree canopy size and position with greater accuracy. Where sufficient information is available in the training data  704 , the vegetation detection model  712  can be trained to characterize vegetation size, classify vegetation type (e.g., tree, bush, shrub, grass, flowers, etc.), and/or classify a species of vegetation. The training process  702  can train the vegetation detection model  712  using machine-learning techniques, such as image segmentation with masking and regions with convolutional neural networks or other such techniques to support vegetation detection based on image data from the aerial imagery data  119 . The vegetation detection model  712  can also be trained beyond two-dimensional canopy detection and may use infrared and/or point-cloud data (e.g., height/depth data) for enhanced feature detection, if available in the aerial imagery data  119  or another source. Infrared data can be used, for instance, to distinguish between live versus dead vegetation and different vegetation species. Point-cloud data can be used to identify the height of vegetation and shrubs and may assist in identifying other characteristics, such as tracking rate of growth over a period of time. 
     The model data  708  can include the merged model data  610 ,  614  of  FIG. 6  or other such data available to apply the trained models  706 . Datasets  716  and location specific data  718  are embodiments of the datasets  122  and location specific data  124  of  FIG. 1 , where data preprocessing  714  can be applied to produce the model data  708 . The data preprocessing  714  can include the data merging  608 ,  612  of  FIG. 6  and creation or access of one or more three-dimensional models  720 , such as the three-dimensional model  502  of building  402  of  FIG. 5  and three-dimensional model  508  of vegetation  408  of  FIG. 5 . 
     Applying the trained models  706  to the model data  708  can result in model predictions  722 . The model predictions  722  can predict whether a pixel of image data is likely part of a building, for example, and whether the pixel represents a feature, such as a roof, siding, decking, door, or window, for instance. As greater details are refined, the trained models  706  can make more specific predictions for one or more derived characteristics of a building, such as a roofing material, a roof shape, a siding material, and a chimney condition. For vegetation, the model predictions  722  can identify whether a pixel is likely part of a particular type of plant, ground covering, or tree, along with finer details, such as branches. The trained models  706  may also predict whether pixels represent one or more property features, such as one or more of a deck, a shed, a pool, a patio, a garage, a playscape, a greenhouse, a fence, a driveway, a vehicle, a fire hydrant, a water source, a fuel storage source, an unknown structure, and/or a property contents. The results of model predictions  722  can be further conditioned by result postprocessing  724 . 
     The result postprocessing  724  can cross-compare results of the model predictions  722  to make a final determination of the most likely feature and/or condition captured by a pixel or group of pixels. The result postprocessing  724  can summarize results to highlight regions, such as pixels collectively grouped as a roof of a single structure, as well as other associated data or a tree canopy, for example. The result postprocessing  724  may also perform comparisons and computations of results between the model predictions  722 , such as determining an estimated distance between one or more vegetation areas and a building (e.g., nearest portion of a building footprint) as separation data  725 . To enhance prediction confidence, model data  708  or inputs to the model data  708  can be rotated, and the model predictions  722  can be performed after each rotation. For example, using the same image (including one or more layers), rotations in increments of ninety degrees can be analyzed with model predictions  722  to confirm whether identified features or conditions are consistently observed with a similar level of confidence. This can help to reduce the impact of shadows resulting in false positives. If, for instance, a tree is found, to identify the canopy of the tree, multiple iterations of analysis with rotations can be used to confirm that the canopy shape identified is consistent with a confidence level at or above a confidence threshold. In the example of an initial image analysis with model predictions  722  followed by three ninety-degree rotation analysis iterations, if a feature or characteristic is identified (e.g., with a confidence &gt;=a confidence threshold) using the building detection model  710  or vegetation detection model  712  in all four or three out of four iterations of model predictions  722 , then the feature or characteristic is confirmed. If the feature or characteristic is only identified for half or fewer iterations of model predictions  722 , then the feature or characteristic is unconfirmed and may not be used in further processing as part of the result postprocessing  724 . 
     The result postprocessing  724  can also compare the separation data  725  to a defensible space guideline  726  to determine a defensible space adherence score  728 . Further processes managed by the process controller  128  of  FIG. 1  can take additional actions, such as generating a wildfire risk map (e.g., wildfire risk map  800  of  FIG. 8 ) including the defensible space adherence score  728  associated with a geographic area and constrained by property boundaries, as further described with respect to  FIG. 8 . 
       FIG. 8  depicts a wildfire risk map  800  according to some embodiments. The wildfire risk map  800  can be generated by the data processing server  102  of  FIG. 1  responsive to the process controller  128  of  FIG. 1 . In the example of  FIG. 8 , the wildfire risk map  800  includes property boundaries  802  with building footprints  804 , including building footprint  804 A,  804 B,  804 C, and  804 D for properties  805 A,  805 B,  805 C, and  805 D. The building footprints  804  can be any type of structure, such as a house, a multi-family dwelling, an apartment building, a commercial building, and the like. The wildfire risk map  800  can also depict a plurality of trees  806  as examples of vegetation. Separation distances between trees  806  (or other vegetation) and building footprints  804  can be computed as separation data, but may not be visible on the wildfire risk map  800 . For example, an estimated distance between each of the one or more trees  806  and a nearest portion of the building footprint  804  can be determined as separation data. Neighboring tree pairs  807  (or other vegetation) can be analyzed to determine distances between multiple trees  806 . The separation data can be compared to a defensible space guideline to determine a defensible space adherence score  808 A,  808 B,  808 C,  808 D associated with each of the properties  805 A,  805 B,  805 C,  805 D. The wildfire risk map  800  can be generated with the defensible space adherence scores  808 A,  808 B,  808 C,  8080 D associated with a geographic area and constrained by the property boundaries  802 . Data in the wildfire risk map  800  can be used for various purposes, such as predicting a fire path spread pattern  810  between the one or more neighboring properties  805 A- 805 D based on geographic features in combination with building footprints  804 A- 804 D, trees  806 , other objects, structures, vegetation, and other such data. 
       FIG. 9  depicts an example of geographic features  900  that can impact a fire path spread pattern  902  according to some embodiments. In  FIG. 9 , geographic features  900  can include one or more of: an elevation  904 , a body of water  906 , and a type of ground covering  908 . The geographic features  900  may be observed in a combination of image data and map data from the aerial imagery data  119  and/or maps  123  of  FIG. 1  for the AI models  126  of  FIG. 1  to predict the fire path spread pattern  902 . Changes in elevation  904 , such as hills and valleys can impact the fire path spread pattern  902  of a wildfire  901 . For example, the elevation  904  may impact spreading due to wind patterns, vegetation  910  and impediments, such as rocks  912 . Bodies of water  906  can include, for instance, streams, rivers, ponds, lakes, oceans, and the like. The type of ground covering  908  can be grass, brush, sand, gravel, dirt, and the like. In some embodiments, weather data  914  can be used in predicting the fire path spread pattern  902 , for instance, where precipitation is likely, where thunderstorms are likely, where hot/dry weather is expected, and other such patterns and trends over a period of time. The weather data  914  can be captured and retrieved from the maps  123  of  FIG. 1  or otherwise observed/forecast. 
       FIG. 10  depicts a remote user interface  1000  example according to some embodiments. The remote user interface  1000  is an example of the remote user interface  127  of  FIG. 1  where one of the remote user systems  125  is depicted as a mobile user device  1002 . The remote user interface  1000  can output a notification  1004  of a fire event and a fire spread path with a fire arrival time  1006  and a recommended course of action  1008 . Other information and content can be output to the remote user interface  1000 . The content provided to the remote user interface  1000  may be determined and transmitted from the data processing server  102  of  FIG. 1 . Alternatively, a third-party server  116  of  FIG. 1  can produce content for the remote user interface  1000  based on data produced by the data processing server  102 , such as the wildfire risk map  800 , fire path spread pattern  810 , and location of properties  805 A- 805 D of  FIG. 8 . Alerts can be generated for targeted users based on a specific risk or can be distributed as community alerts to neighborhoods and/or wider-scale areas. For example, the data processing server  102  of  FIG. 1  may send targeted notifications to designated government entities or community organizations associated with a geographic area, which can then relay and distribute the notifications to specific neighborhoods. 
       FIG. 11  depicts a user interface  1100  according to some embodiments. In the example of  FIG. 11 , the user interface  1100  can be used to allow a user to select details of an image of the aerial imagery data  119  of  FIG. 1  through the user application  132  of  FIG. 1  as part of a wildfire risk map to analyze. The user interface  1100  can provide a graphical user interface  1102  to select commands, provide address input, and control image viewing, such as zoom controls and making different features or layers visible on the user interface  1100 . The example of  FIG. 11  illustrates a plurality of properties  1104  with property boundaries  1106  and wildfire risk scores  1108 , such as defensible space adherence scores for a selected geographic area. The wildfire risk scores  1108  can indicate whether an underlying property  1104  is at higher or lower risk of wildfire impact to dwellings or other structures, for instance based on meeting a defensible space guideline  726  and/or other factors that impact fire spread. For instance, wildfire risk scores  1108  may also incorporate a fire risk adjustment based on moisture content of vegetation in proximity to buildings, structures, and/or other objects. Other features and derived characteristics may also or alternatively be displayed through the user interface  1100 . Although one example is depicted in  FIG. 11 , it will be understood that many variations are contemplated, including additional interfaces, command options, and identification options. 
       FIG. 12  depicts a process  1200  of using wildfire risk analysis for rating and quoting according to some embodiments. The process  1200  can be performed, for example, by the system  100  of  FIG. 1 . Visual high resolution aerial imagery is queried  1202  for a location of interest, for instance, from aerial imagery data  119  of  FIG. 1 . Information on individual locations  1204  can be captured on a location-by-location basis and may be accessed from the property data  121  of  FIG. 1 . A data merging process  1206  can combine the data and pass merged data through one or more AI models  126  of  FIG. 1  to test for the presence of particular attributes at block  1208 , such as separation data based on an estimated distance between one or more trees and a nearest portion of a building footprint. Separation may also be determined in three-dimensional space between buildings and vegetation areas. The separation data can be compared to a defensible space guideline to determine a defensible space adherence score. The output of the AI models  126  can be the defensible space adherence score or other such wildfire-related scores that are fed to a rate-quote-issue system  1210 , which may be part of system  100  of  FIG. 1  or located in another networked environment. In some embodiments, a processing request output to the rate-quote-issue system  1210  can include population of one or more electronic forms in the rate-quote-issue system  1210  as a second system based on a record resulting from the AI models  126  at block  1208 . Scores from the rate-quote-issue system  1210  can be fed to a rating engine  1212 . The rate-quote-issue system  1210  and/or the rating engine  1212  can apply other models (e.g., risk models, loss models) to use the defensible space adherence score or other such wildfire-related scores in determining loss risk scores, potential loss amounts, and other such values. The rating engine  1212  can apply the scores to determine whether a risk threshold is met or exceeded and determine potential limits to apply if a quote is generated. A quote can be submitted  1214  from the rating engine  1212  to an end user/customer. The process  1200  can be initiated through the user application  132  and managed by the process controller  128  of  FIG. 1  in combination with one or more other applications (not depicted). 
     Turning now to  FIGS. 13A and 13B , a process flow  1300  is depicted according to an embodiment. The process flow  1300  includes a number of steps that may be performed in the depicted sequence or in an alternate sequence. The process flow  1300  may be performed by the system  100  of  FIG. 1 . In one embodiment, the process flow  1300  is performed by the data processing server  102  of  FIG. 1  in combination with the one or more user systems  106  and/or the one or more data storage servers  110 . The process flow  1300  is described in reference to  FIGS. 1-13B . 
     At step  1302 , the data processing server  102  can access a first dataset including aerial imagery data  119  associated with a geographic area. The first dataset can also include infrared data associated with the geographic area. At step  1304 , the data processing server  102  can access a second dataset including property boundary data associated with the geographic area. The aerial imagery data  119  can be accessed through one or more third-party services  118  or from a local copy of datasets  122  in a data cache  120 . The property boundary data can be accessed from property data  121  through one or more third-party services  118  or from a local copy of datasets  122  in a data cache  120 . Where multiple viewing perspectives are used, the aerial imagery data  119  associated with the geographic area from the first viewing perspective and the second viewing perspective can be aligned based on one or more grid patterns  300 ,  400 . 
     At step  1306 , the data processing server  102  can identify a plurality of property boundaries  802 ,  1106  associated with the geographic area based on the property boundary data. At step  1308 , the data processing server  102  can apply a building detection model  710  to identify a building (for instance, as a building footprint  804 ) based on the first dataset and constrained by the property boundaries  802 ,  1106 . The building detection model  710  can include an artificial intelligence model that predicts a roof location of the building based on individual pixel data and an aggregation of the individual pixel data, for example, to establish the building footprint  804 . The property boundaries  802 ,  1106  can be defined as irregular polygons and can include partial boundaries when mapped to the geographic location covered by the datasets. Coordinate transformations or other map adjustment techniques can be used to establish spatial alignment between the property boundaries  802 ,  1106  and property features with respect to the datasets. 
     At step  1310 , the data processing server  102  can apply a vegetation detection model  712  to identify one or more vegetation areas (such as trees  806  or other plant life) based on the first dataset and constrained by the property boundaries  802 ,  1106 . A moisture content of the one or more vegetation areas can be identified by the vegetation detection model  712  based on the infrared data of the first dataset in combination with classifying vegetation type of the one or more vegetation areas. Reflectance changes over time can be used to characterize changes in moisture content of vegetation in the infrared band, for example. At step  1312 , the data processing server  102  can determine an estimated distance between each of the one or more vegetation areas (such as trees  806  or other plant life) and the building (for instance, a nearest portion of the building footprint  804  or a targeted building feature) as separation data, such as separation data  725 . The separation data  725  may include a plurality of distance estimates between various features. For instance, the data processing server  102  can identify one or more neighboring tree pairs  807  based on a location of each of the one or more trees  806 , determine an estimated tree-to-tree distance for the one or more neighboring tree pairs  807 , and incorporate the estimated tree-to-tree distance into the separation data  725 . At step  1314 , the data processing server  102  can compare the separation data  725  to a defensible space guideline  726  to determine a defensible space adherence score  728 , for instance, as part of result postprocessing  724 . The data processing server  102  can also determine a fire risk adjustment based on the moisture content. At step  1316 , the data processing server  102  can generate a wildfire risk map  800 , including the defensible space adherence score  808 A- 808 D associated with the geographic area, which can be constrained by the property boundaries  802 ,  1106 . The wildfire risk map  800  can incorporate the fire risk adjustment. The data processing server  102  may also receive an update to the first dataset, compare the update to the first dataset with a previous version of the first dataset (e.g., stored in datasets  122 ), identify one or more changes between the previous version of the first dataset and the update to the first dataset, and modify the wildfire risk map  800  based on the one or more changes. 
     The data processing server  102  can also create a record including an indicator of the geographic area, the property  805 A- 805 D, and the defensible space adherence score  808 A- 808 D. The record can be held temporarily in the data cache  120  and/or may be captured for longer term retention in the data storage  134 . The data processing server  102  can generate a processing request based on the record. The processing request can include, for example, population of one or more electronic forms in a second system, such as the rate-quote-issue system  1210 , based on the record. Further, the processing request can include a scoring computation based on comparing contents of the record to one or more scoring thresholds, forwarding a result of the scoring computation with the record for a quote, and sending the quote to a user identifier associated with the property  805 A- 805 D, for instance, as part of process  1200 . 
     The process flow  1300  can be performed responsive to user requests through one or more user applications  132 . The data processing server  102  and/or one or more user systems  106  can provide an interactive interface through a graphical user interface, such as user interface  1100 . The interactive user interface can highlight the building footprint  804  and/or other features on the graphical user interface  1102 . The geographic area can also be identified on the interactive interface based on a user input at the graphical user interface  1102 . In some embodiments, the data processing server  102  can perform batch processing for a plurality of properties to create a plurality of records and generate a sequence of processing requests based on the records. 
     Process flow  1300  can be further enhanced to include one or more steps of processes  1400 ,  1500 ,  1600 , and/or  1700  of  FIGS. 14, 15, 16, and 17 . Although processes  1400 ,  1500 ,  1600 , and  1700  are illustrated as sequential flows, various steps of processes  1400 ,  1500 ,  1600 , and  1700  can be selectively performed, combined, or omitted in embodiments. Further, steps of processes  1400 ,  1500 ,  1600 , and  1700  can be incorporated within the process flow  1300  of  FIGS. 13A and 13B  or performed separately. 
     In reference to process  1400 , at step  1402 , the data processing server  102  can identify one or more neighboring properties  805 A- 805 D that share at least one of the property boundaries  802 ,  1106 . At step  1404 , the data processing server  102  can perform a cross-property separation analysis with respect to the one or more neighboring properties  805 A- 805 D. The cross-property separation analysis can include determining a shortest distance between the building footprint  804  and a structure on the one or more neighboring properties  805 A- 805 D, such as a building, a garage, a shed, a deck, and the like. The cross-property separation analysis can include determining a shortest distance between the building footprint  804  and one or more trees  806  on the one or more neighboring properties  805 A- 805 D. The cross-property separation analysis may include determining an estimated tree-to-tree distance with respect to the one or more trees  806  on the one or more neighboring properties  805 A- 805 D. At step  1406 , the data processing server  102  can incorporate a result of the cross-property separation analysis into the separation data  725 . 
     At step  1408 , the data processing server  102  can access a third dataset including a plurality of geographic features  900  associated with the geographic area, which may be accessed from one or more maps  123  through third-party services  118 . The geographic features  900  can include, for example, one or more of: an elevation  904 , a body of water  906 , and a type of ground covering  908 . At step  1410 , the data processing server  102  can predict a fire path spread pattern  810 ,  902  between the one or more neighboring properties  805 A- 805 D based on the geographic features  900  identified in the third dataset. For instance, a chain of properties  805 A- 805 D having defensible space adherence scores indicative of a greater wildfire risk can be used to establish a higher likelihood of a path for a wildfire  901  to spread. Further, factors, such as a higher density of vegetation, type of ground covering  908 , changes in elevation  904 , and obstacles that impede fire spreading, such as bodies of water  906  can impact the projected direction and rate of spreading predicted for a wildfire  901 . Known fire path spreading pattern determination algorithms can also be incorporated into the analysis to enhance prediction accuracy. 
     In reference to process  1500 , at step  1502 , the data processing server  102  can construct a three-dimensional model  500 ,  720  of the geographic area based on the aerial imagery data  119 . The three-dimensional model  500 ,  720  can be created or updated as part of data preprocessing  714 . At step  1504 , the data processing server  102  can perform a three-dimensional analysis based on the three-dimensional model  500 ,  720  to determine the separation data  725 . The three-dimensional analysis can be performed as part of the result postprocessing  724 . As an example, the dataset selected for analysis (e.g., a first dataset) can include a plurality of height data on a per-pixel basis. At step  1506 , the data processing server  102  can determine a size-based component of a wildfire risk score based on a location, area, and height of vegetation  408  captured in the three-dimensional model  500 ,  720 . At step  1508 , the data processing server  102  can predict a reduction in the wildfire risk score based on reducing either or both of the area and height of vegetation  408 . For instance, reducing a tree canopy size of one or more trees  806  in close proximity to a building footprint  804 A- 804 D of a building  402  or house  406  may result in an anticipated reduction in the wildfire score for the associated property  805 A- 805 D. At step  1510 , the data processing server  102  can output a vegetation pruning recommendation with the wildfire risk map  800  to illustrate the predicted reduction in the wildfire risk score by performing a size reduction of the vegetation  408 , for instance on the remote user interface  1000  or user interface  1100 . Where portions of the vegetation  408  are identified as dead, dying, or low on moisture content, the impact of pruning recommendations can be more substantial. In some embodiments, subsequent images can be captured for the same location at a later time to determine whether the recommendations were followed and if the wildfire risk score changed. 
     In reference to process  1600 , at step  1602 , the data processing server  102  can identify one or more dead spots  510  in the one or more trees  806  or other vegetation based on the infrared data. At step  1604 , the data processing server  102  can determine a fire risk adjustment based on the one or more dead spots  510 . At step  1606 , the data processing server  102  can incorporate the fire risk adjustment into the wildfire risk map  800 . At step  1608 , the data processing server  102  can identify a ground covering moisture content based on the infrared data. At step  1610 , the data processing server  102  can incorporate a predicted impact of the ground covering moisture content in the wildfire risk map  800 . 
     In reference to process  1700 , at step  1702 , the data processing server  102  can monitor for a fire event proximate to the geographic area. At step  1704 , the data processing server  102  can predict a fire spread path based on the fire event and the wildfire risk map  800 , such as fire path spread pattern  810 ,  902  with respect to a wildfire  901 . At step  1706 , the data processing server  102  can output a notification  1004  of the fire event and the fire spread path to a user interface, such as remote user interface  1000 . At step  1708 , the data processing server  102  can determine a current weather condition and a forecast weather condition between a location of the fire event and the geographic area, for instance, based on weather data  914 . At step  1710 , the data processing server  102  can predict a rate of fire spreading on the fire spread path based on the current weather condition and the forecast weather condition. The rate can be impacted by expected precipitation, absence of precipitation, windspeed, wind direction, and the like. At step  1712 , the data processing server  102  can predict a fire arrival time  1006  based on the rate of fire spreading. At step  1714 , the data processing server  102  can output the prediction of the fire arrival time  1006  with the notification  1004  of the fire event and the fire spread path to the user interface. A recommended course of action  1008  can also be output to the remote user interface  1000 . 
     In some embodiments, the data processing server  102  can access a plurality of datasets from an archive including the aerial imagery data and the infrared data associated with the geographic area and collected over a period of time, for instance, from a third-party service  118  of  FIG. 1 . The defensible space adherence score and the fire risk adjustment can be determined over the period of time based on the datasets from the archive. One or more areas of change can be identified with respect to landscape management for wildfire defense based on the defensible space adherence score and the fire risk adjustment over the period of time. A future fire risk of the geographic area or a neighboring geographic area can be predicted based on identifying the one or more areas of change and change trends or patterns over the period of time. An alert notification can be triggered based on the future fire risk exceeding an alert threshold. The alert threshold can be established by examining data from previously observed wildfires and/or other information determined to have a causal relationship with fire spread risk. Further aspects can include identifying one or more dead spots in the one or more vegetation areas based on the moisture content and determining the fire risk adjustment based on a distance of the one or more dead spots to a building. 
     Various property conditions can be used to set or adjust a fire risk. For example, the data processing server  102  can determine a condition of a building, and the fire risk adjustment can be determined based at least in part on the condition of the building. For instance, the condition of the building can be determined based on identifying one or more of: a roof shape of the building, a chimney state of the building, and/or a roof vent of the building. As another example, the building detection model  710  of  FIG. 7  can be trained to identify various building configurations, materials, and conditions. For instance, a roofing material of the building can be identified based on the building detection model  710 . A roof condition can be detected, such as deterioration or damage to the roofing material, and the fire risk adjustment can be determined based at least in part on the roof condition. As another example, the condition of the building can be determined based on one or more changes over time in the aerial imagery data  119  of  FIG. 1  of multiple datasets associated with the building. Further, the condition of the building can be associated with a deck of the building, such as a deck material, a deck deterioration, a deck enclosure, a deck elevation, a deck contents, and other such detectable features. 
     In some embodiments, the data processing server  102  can identify one or more objects external to a building, for instance, using AI models  126  of  FIG. 1 . The wildfire risk map  800  can be adjusted based on the one or more objects. For instance, the one or more objects can include at least one fire hydrant or water source. Further, the one or more objects can include at least one fuel storage source. The one or more objects may also include at least one vehicle, such as car  404  of  FIG. 4 . 
     In some embodiments, the vegetation detection model  712  can include a three-dimensional model configured to characterize a vegetation size and classify a vegetation type. The vegetation type can be classified as a species, and the species can map to a wildfire risk with respect to the moisture content. Mapping of vegetation species to wildfire risk can be identified through querying one or more data sources, for instance, from third-party services  118  of  FIG. 1 . 
     In some embodiments, the AI models  126  of  FIG. 1  can be trained to identify a location of an electrical or gas utility connection at a building. The defensible space adherence score can be based on a distance from the location to the one or more vegetation areas. The fire risk can also be adjusted based on detecting a deterioration or other condition in proximity to the location of the electrical or gas utility connection. For instance, where overhead powerlines are used to connect to a building, the presence of trees or other vegetation in proximity to the overhead powerlines can result in an increased fire risk where a possible powerline breakage (e.g., due to tree damage) may result in a live wire contacting vegetation or a combustible object. 
     Technical effects include automated detection of features in image data that may not be readily observed and understood by a human observer without extensive additional analysis. Automated feature detection and construction of three-dimensional models can enable higher-level analysis functions to derive additional characteristics that may not be apparent in separate datasets. Analysis results can be used to determine compliance with guidelines, a wildfire risk, and predict arrival of a wildfire. 
     It will be appreciated that aspects of the present invention may be embodied as a system, method, or computer program product and may take the form of a hardware embodiment, a software embodiment (including firmware, resident software, micro-code, etc.), or a combination thereof. Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     One or more computer readable medium(s) may be utilized. The computer readable medium may comprise a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may comprise, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In one aspect, the computer readable storage medium may comprise a tangible medium containing or storing a program for use by or in connection with an instruction execution system, apparatus, and/or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may comprise any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, and/or transport a program for use by or in connection with an instruction execution system, apparatus, and/or device. 
     The computer readable medium may contain program code embodied thereon, which may be transmitted using any appropriate medium, including, but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. In addition, computer program code for carrying out operations for implementing aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer, or entirely on the remote computer or server. 
     It will be appreciated that aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products, according to embodiments of the invention. It will be understood that each block or step of the flowchart illustrations and/or block diagrams, and combinations of blocks or steps in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     In addition, some embodiments described herein are associated with an “indication”. As used herein, the term “indication” may be used to refer to any indicia and/or other information indicative of or associated with a subject, item, entity, and/or other object and/or idea. As used herein, the phrases “information indicative of” and “indicia” may be used to refer to any information that represents, describes, and/or is otherwise associated with a related entity, subject, or object. Indicia of information may include, for example, a code, a reference, a link, a signal, an identifier, and/or any combination thereof and/or any other informative representation associated with the information. In some embodiments, indicia of information (or indicative of the information) may be or include the information itself and/or any portion or component of the information. In some embodiments, an indication may include a request, a solicitation, a broadcast, and/or any other form of information gathering and/or dissemination. 
     Numerous embodiments are described in this patent application, and are presented for illustrative purposes only. The described embodiments are not, and are not intended to be, limiting in any sense. The presently disclosed invention(s) are widely applicable to numerous embodiments, as is readily apparent from the disclosure. One of ordinary skill in the art will recognize that the disclosed invention(s) may be practiced with various modifications and alterations, such as structural, logical, software, and electrical modifications. Although particular features of the disclosed invention(s) may be described with reference to one or more particular embodiments and/or drawings, it should be understood that such features are not limited to usage in the one or more particular embodiments or drawings with reference to which they are described, unless expressly specified otherwise. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. On the contrary, such devices need only transmit to each other as necessary or desirable, and may actually refrain from exchanging data most of the time. For example, a machine in communication with another machine via the Internet may not transmit data to the other machine for weeks at a time. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries. 
     A description of an embodiment with several components or features does not imply that all or even any of such components and/or features are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the present invention(s). Unless otherwise specified explicitly, no component and/or feature is essential or required. 
     Further, although process steps, algorithms or the like may be described in a sequential order, such processes may be configured to work in different orders. In other words, any sequence or order of steps that may be explicitly described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to the invention, and does not imply that the illustrated process is preferred. 
     “Determining” something can be performed in a variety of manners and therefore the term “determining” (and like terms) includes calculating, computing, deriving, looking up (e.g., in a table, database or data structure), ascertaining and the like. 
     It will be readily apparent that the various methods and algorithms described herein may be implemented by, e.g., appropriately and/or specially-programmed computers and/or computing devices. Typically a processor (e.g., one or more microprocessors) will receive instructions from a memory or like device, and execute those instructions, thereby performing one or more processes defined by those instructions. Further, programs that implement such methods and algorithms may be stored and transmitted using a variety of media (e.g., computer readable media) in a number of manners. In some embodiments, hard-wired circuitry or custom hardware may be used in place of, or in combination with, software instructions for implementation of the processes of various embodiments. Thus, embodiments are not limited to any specific combination of hardware and software. 
     A “processor” generally means any one or more microprocessors, CPU devices, computing devices, microcontrollers, digital signal processors, or like devices, as further described herein. 
     The term “computer-readable medium” refers to any medium that participates in providing data (e.g., instructions or other information) that may be read by a computer, a processor or a like device. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include DRAM, which typically constitutes the main memory. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to the processor. Transmission media may include or convey acoustic waves, light waves and electromagnetic emissions, such as those generated during RF and IR data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. 
     The term “computer-readable memory” may generally refer to a subset and/or class of computer-readable medium that does not include transmission media such as waveforms, carrier waves, electromagnetic emissions, etc. Computer-readable memory may typically include physical media upon which data (e.g., instructions or other information) are stored, such as optical or magnetic disks and other persistent memory, DRAM, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, computer hard drives, backup tapes, Universal Serial Bus (USB) memory devices, and the like. 
     Various forms of computer readable media may be involved in carrying data, including sequences of instructions, to a processor. For example, sequences of instruction (i) may be delivered from RAM to a processor, (ii) may be carried over a wireless transmission medium, and/or (iii) may be formatted according to numerous formats, standards or protocols, such as Bluetooth™, TDMA, CDMA, 3G. 
     Where databases are described, it will be understood by one of ordinary skill in the art that (i) alternative database structures to those described may be readily employed, and (ii) other memory structures besides databases may be readily employed. Any illustrations or descriptions of any sample databases presented herein are illustrative arrangements for stored representations of information. Any number of other arrangements may be employed besides those suggested by, e.g., tables illustrated in drawings or elsewhere. Similarly, any illustrated entries of the databases represent exemplary information only; one of ordinary skill in the art will understand that the number and content of the entries can be different from those described herein. Further, despite any depiction of the databases as tables, other formats (including relational databases, object-based models and/or distributed databases) could be used to store and manipulate the data types described herein. Likewise, object methods or behaviors of a database can be used to implement various processes, such as the described herein. In addition, the databases may, in a known manner, be stored locally or remotely from a device that accesses data in such a database. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one more other features, integers, steps, operations, element components, and/or groups thereof.