Patent Application
20200134573
The disclosed principles relates generally to automated systems and methods for identifying roof damages. More specifically, the disclosed principles relate to automated systems and methods for use by roofing service providers to identify the serviceable roofs with damages in a desired geographical area.
Building roofs may get damaged due to various factors, such as, but not limited to, hail events, storms, other weather conditions, long service life, etc. The owners of the buildings need to know if their building was one of those that were actually damaged so that repairs may be made on time and to claim the roofing insurance from the insurance provider. Roofing services providers can utilize this opportunity by identifying the damaged or serviceable roofs in a selected geographical area to sell their roofing services and associated products to perform the replacement or maintenance of the damaged roofs. Thus, identification of the serviceable roofs with damages in a desired area is beneficial for the roofing services providers to increase their sales. There are several prior arts that teach us the identification of roofing features from images of the roofs captured using drones and other aerial image capturing methods. The roofing features identified from the images of the roofs is beneficial for roofing services providers to specify materials and associated costs for both newly-constructed buildings, as well as for replacing and upgrading existing structures. Various software systems have been implemented to process aerial images to identify roofing characteristics of many roofing structures. However, such systems are often time-consuming and difficult to use, and require a great deal of manual input by a user. Further, such systems may not have the ability to improve results through continued usage over time. The following prior arts are hereby incorporated by reference for their supportive teachings of the disclosed principles:
U.S. Pat. No. 8,731,234 titled “Automated Roof Identification Systems And Methods” issued to EagleView Technologies Inc. discloses an automatic roof identification systems and methods. The patent discloses a roof estimation system configured to automatically detect a roof in a target image of a building having a roof. Automatically detecting a roof in a target image includes training one or more artificial intelligence systems to identify likely roof sections of an image. The artificial intelligence systems are trained on historical image data or an operator-specified region of interest within the target image. Then, a likely outline of the roof in the target image can be determined based on the trained artificial intelligence systems. The likely roof outline is used to generate a roof estimate report.
U.S. Pat. No. 9,262,564 titled “Method Of Estimating Damage To A Roof” issued to State Farm Mutual Automobile Insurance Co. discloses a system and a method for estimating damage to a roof. The method includes the steps of generating, from a first point cloud representing a roof, a second point cloud representing a shingle. The system and method further includes comparing the second point cloud to a model point cloud, the model point cloud representing a model shingle. The method also includes identifying, based on the comparison, a first set of points, correlating each point within the first set of points to a representation of a point of damage. The system and method includes identifying a second set of points, the second set of points including at least one point from the first set, correlating the second set of points to a representation of a damaged region of the roof. Further, the method includes generating and storing to a memory a report based on the second set of points for subsequent retrieval and use in estimating damage to at least part of the roof. A damage assessment module operating on a computer system automatically evaluates a roof, estimating damage to the roof by analyzing a point cloud of a roof. The damage assessment module identifies individual shingles from the point cloud and detects potentially damaged areas on each of the shingles. The damage assessment module then maps the potentially damaged areas of each shingle back to the point cloud to determine which areas of the roof are damaged. Based on the estimation, the damage assessment module generates a report on the roof damage.
Another prior art, U.S. Pat. No. 9,613,538 titled “Unmanned Aerial Vehicle Rooftop Inspection System” issued to Unmanned Innovation Inc. discloses methods, systems, and apparatus, including computer programs encoded on computer storage media, for an unmanned aerial system inspection system. One of the methods is performed by a unmanned aerial vehicle (UAV) and includes receiving, by the UAV, flight information describing a job to perform an inspection of a rooftop. The UAV ascends to a particular altitude and an inspection of the rooftop is performed including obtaining sensor information describing the rooftop. Location information identifying a damaged area of the rooftop is also received. An inspection of the damaged area of the rooftop is performed including obtaining detailed sensor information describing the damaged area. The invention utilizes the UAV to schedule inspection jobs and to perform inspections of potentially damaged properties, e.g., a home, an apartment, an office building, a retail establishment, etc. By intelligently scheduling jobs, a large area can be inspected using UAV(s), which reduces the overall time of inspection, and enables property to be maintained in safer conditions. Furthermore, by enabling an operator to intelligently define a safe flight plan of a UAV, and enable the UAV to follow the flight plan and intelligently react to contingencies, the risk of harm to the UAV or damage to surrounding people and property can be greatly reduced.
The disclosed principles relates to a system for identifying buildings that are damaged in a geographic area from a damaging weather event; and more particularly to a system for assisting one or more roofing services providers to sell a variety of roof repairing services and products to a number of relevant customers. All the above incorporated systems and methods can be utilized to identify the damages to the buildings and roofs by random inspection of the buildings and roofs at any particular date or a selected time. However, such methods cannot be utilized to identify the serviceable roofs with damages caused by severe weather activities such as a hailstorm over a large selected geographical area using weather information and data bases. Moreover, the above systems and methods cannot be utilized by the roofing services providers to identify the roof characteristics of a multiple number of roofs of buildings spread over a wide geographical area using weather data. Hence, there exists a need for an automated system and method for assisting the roofing services providers to identify the serviceable roofs with damages caused by severe weather activities such as a hailstorm over one or more geographical areas using weather historical data. Moreover, the needed system and method would provide location information of the identified serviceable roofs with damages to enable the roofing services providers to sell roof repairing services and products to the relevant customers. Furthermore, the needed system and method would also assist the owners of the building to claim the existing roofing insurance from their insurance providers on time to perform maintenance on the damaged roofs.
The present systems in accordance with the disclosed principles for assisting the roofing services providers to sell roof repairing services and products to the relevant customers with one or more serviceable roofs includes an electronic computing device having a memory unit configured to store a number of instructions of an application for identifying the serviceable roofs in a selected geographical area. The instructions of the application stored in the memory unit includes a number of artificial intelligence (AI)-based image processing instructions, which are executable using a processor associated with the electronic computing device. The roofing services provider can launch the application from the electronic computing device to execute the artificial intelligence-based image processing instructions of the application, and to perform a number of tasks including capturing a number of images of the roofs in a selected geographical area. The images of the roofs are obtained from a series of time-lapse images, which are captured from a number of past and real-time satellite images of the geographical area, captured over a preset period of time. As used herein, any reference to images or imaging includes any and all imaging technologies, and any images resulting therefrom, using any type of imaging technology either now existing or later developed. The artificial intelligence-based instructions of the application processes the images of the roofs to identify a number of roof characteristics associated with each of the roofs. The roof characteristics are identified by comparing a number of features associated with each of the roofs, identified from the series of time-lapse images of the roofs, with a number of predefined roof features associated with a number of roof types stored in a dynamically updated database associated with the present application.
Further, the present application receives the weather data of the geographical area over the preset period of time from one or more weather data service providers. The weather data may include a variety of weather activities such as hailstorm activities capable of damaging the different roofs under analysis. Each of the roofs from the images are converted through a number of image conversion steps including an image pixilation step to identify one or more damages on the roofs caused by the severe weather activity in the selected geographical area. The present application identifies the serviceable roofs with damages by analyzing a number of sequential changes in respective pixels of the series of time-lapse images and correlating with the roof characteristics and the weather activities during the series of time-lapse images capable of damaging the particular roof type. Once the present system identifies the serviceable roofs with damages, the location information associated with a property having the serviceable roof is obtained from the past and real-time satellite images. This roofing services provider utilizes this information to locate the respective buildings and present the information to the owners of the buildings to assist them in getting the roofing services claims to perform the relevant roof maintenance. This in turn improves the sales and services of the roofing services provider.
The disclosed principles also relate to a computer-implemented method for assisting the roofing services providers to sell their roof repairing services and products to relevant customers. The method includes the steps of providing the roofing service provider with an application configured to run on an electronic computing device for identifying the serviceable roofs in a geographical area. The roofing service provider can launch the application using the electronic computing device to capture the aerial images of the roofs in the geographical area. The roofing service provider can further select a desired time period for capturing the images of the roofs in the geographical area. The images of the roofs form a series of time-lapse images, obtained from the past and real-time satellite images of the geographical area, captured over the desired time period set by the roofing services provider. Now the artificial intelligence-based instructions of the application analyses the roofs in the images and identifies the roof characteristics of the each of the roofs in the image. The application also receives the weather data including the weather activities, during the preset time period, capable of damaging roof types present in the image. This helps the roofing services providers to identify the roofs, which are at high risk of failure or getting damaged due to the severe weather activity during the present period. The application identifies the damages on the roofs, mainly caused by the weather activities, by automated conversion of the series of time-lapse images through a number of image conversion steps including an image pixilation step. The application identifies the serviceable roofs with damages by analyzing a number of sequential changes in respective pixels of the series of time-lapse images and correlating with the roof characteristics and the weather activities during the series of time-lapse images capable of damaging the particular roof type. Once the serviceable roofs with damages are identified, the location information associated with that property is identified, which is further utilized by the roofing services provider to contact the owners of the buildings to assist them in getting the roofing services claims and to perform the relevant roof maintenance on the damaged roof.
Other features of the disclosed principles are discussed below. The disclosed principles are designed to fulfill the below and other additional features as detailed in the following claims section and detailed description section of the present disclosure.
One feature of the disclosed principles provides artificial intelligence-based systems for assisting the roofing services providers to identify the serviceable roofs with damages in a selected geographical area.
Another feature of the disclosed principles provides an electronic computing device running an application for identifying the serviceable roofs with damages in a selected geographical area from past and real-time satellite images of the geographical area.
Another feature of the disclosed principles provides an electronic computing device running an application for identifying roof characteristics including roof type, material, age and other relevant information associated with a number of roofs present in a selected geographical area.
Another feature of the disclosed principles provides an electronic computing device running an artificial intelligence-based self-learning application for identifying the serviceable roofs with damages in a selected geographical area.
Another feature of the disclosed principles provides an provides an electronic computing device running an artificial intelligence-based application for identifying the damages on the roofs caused by severe weather activities in the geographical area.
Another feature of the disclosed principles provides electronic computing device running an artificial intelligence-based application for predicting the serviceable roofs in a geographical area and a number of roofing maintenance related information associated with the serviceable roofs.
Another feature of the disclosed principles provides a system having an electronic computing device running an artificial intelligence-based application for transforming the images of the roofs through a series of steps including image pixilation to identify the serviceable roofs with damages in a geographical area.
Another feature of the disclosed principles provides an artificial intelligence-based application with a dynamic graphical user interface for allowing the roofing services providers to manually analyze the serviceable roofs with damages in a geographical area.
Another feature of the disclosed principles provides a method for assisting the roofing series providers to identify the location information of the serviceable roofs with damages within a geographical area for selling their roofing products and services.
Another feature of the disclosed principles provides a method for alerting the roofing services providers to identify the location information of the serviceable roofs with damages within a geographical area and assisting them to contact the owners of the buildings within a specific time period for availing the roofing insurance claims.
Another feature is to provide a system for identifying buildings that are damaged in a geographic area from a damaging weather event, comprising: a) accessing weather data and identifying a date of the damaging weather event; b) accessing geographic data for identifying the geographic area where the damaging weather event occurred; c) accessing visual data of buildings where the damaging weather event occurred; and d) identifying an individual building that was damaged based on the visual data, geographic data and weather data. Wherein the visual data involves visual images of roofs of buildings. Wherein the damaging weather event is caused by hail striking the buildings. Wherein a global mapping service provides the visual data and geographic data. Wherein the weather data is at least in part derived from NOAA (national oceanic and atmospheric administration) collected data. Wherein, accessing the visual data of the buildings that were in the damaging weather event is examined before and after the date of the damaging weather event.
These, together with other features of the disclosed principles, along with the various features of novelty, which characterize the disclosed principles, are pointed out with particularity in the disclosure. For a better understanding of the disclosed principles, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the systems and methods according to the disclosed principles. In this respect, before explaining at least one embodiment of the disclosed principles in detail, it is to be understood that the disclosed principles are not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The disclosed principles are capable of other embodiments and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
To further clarify various aspects of some example embodiments of the disclosed principles, a more particular description of the disclosed principles will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawing. It is appreciated that the drawing depicts only illustrated embodiments of the disclosed principles and are therefore not to be considered limiting of its scope. Elements in the figures have not necessarily been drawn to scale in order to enhance their clarity and improve understanding of these various elements and embodiments of the disclosed principles. Furthermore, elements that are known to be common and well understood to those in the industry may not be depicted in order to provide a clear view of the various embodiments of the disclosed principles, thus the drawings are generalized in form in the interest of clarity and conciseness. The disclosed principles will be described and explained with additional specificity and detail through the use of the accompanying drawing in which:
In the following discussion that addresses a number of embodiments and applications of the disclosed principles, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments in which the disclosed principles may be practiced. It is to be understood that other embodiments may be utilized and changes may be made without departing from the scope of the disclosed principles. The embodiments of the present disclosure described below are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art may appreciate and understand the principles and practices of the present disclosure.
Further, various inventive features are described below that can each be used independently of one another or in combination with other features. However, any single inventive feature may not address any of the problems discussed above or only address one of the problems discussed above. Further, one or more of the problems discussed above may not be fully addressed by any of the features described below. The following embodiments and the accompanying drawings, which are incorporated into and form part of this disclosure, illustrate one or more embodiments of the disclosed principles and together with the description, serve to explain the disclosed principles. To the accomplishment of the foregoing and related ends, certain illustrative aspects of the disclosed principles are described herein in connection with the following description and the annexed drawings. These aspects are indicative, however, of but a few of the various ways in which the disclosed principles can be employed and are intended to include all such aspects and their equivalents. Other advantages and novel features of the disclosed principles will become apparent from the following detailed description of the disclosed principles when considered in conjunction with the drawings.
Further, the following section summarizes some aspects of the present disclosure and briefly introduces some preferred embodiments. Simplifications or omissions in this section as well as in the abstract or the title of this description may be made to avoid obscuring the purpose of this section, the abstract and the title. Such simplifications or omissions are not intended to limit the scope of the present disclosure nor imply any limitations.
The disclosed principles relate to systems and methods for assisting one or more roofing services providers to find one or more buildings having a number of serviceable roofs within a particular geographical area. The identification of the buildings with the serviceable roofs enables the roofing service providers to contact the owners or the building or to find potential customers to sell a variety of roof repairing services and products to the customers. Further, the disclosed systems and methods enable the roofing services providers to send their sales personnel to visit the buildings with the serviceable roofs and visually present the images of the damages on the roofs for performing the much-needed maintenance of the roof of the building. In some cases, the harsh weather activities such as the hailstorm, wind, rain and other weather activities damage the roofs or a part of the roofs of the buildings. The present systems and methods enable the identification of the damages to the roofs after each of these weather activities and presents the damage related information to the customers for performing the needed roof maintenance on time. In some instances, the present systems and methods enable the customers or owners of the buildings with serviceable roofs to request for roof insurance claims based on the severity of damages on the roofs caused by the harsh weather activities such as, but not limited to, the hailstorm with large size hail stones capable of damaging the roofs. Thus, the present systems and methods enable the customers or owners of the buildings to properly maintain the building roofs to improve safety, life span and reduce the overall maintenance and replacement cost of the roof of the building. Further, the present systems and methods improve the sales and profitability of the roofing services providers and assists to provide better service to the customers.
Referring now to
The instructions of the present application 120 for identifying the serviceable roofs with damages, when executed using the processor, performs a number of automated tasks such as, but not limited to, capturing one or more images of the roofs of the buildings in the geographical area. In an exemplary embodiment of the disclosed principles, the images of the roofs of the buildings in a desired geographical area is obtained from one or more aerial images covering the geographical area. In some instances, the images captured using the present application 120 includes a series of aerial images of the geographical area obtained from one or more satellite images captured using one or more satellites 200 covering the particular geographical area 206. In some other instances, the present application 120 captures the images in form of a series of time-lapse images from a series of past and real-time satellite images, captured over a period of time, covering the geographical area. As used herein, such images or image-capturing technology may encompass any and all imaging technologies, and any images resulting therefrom, using any type of imaging technology either now existing or later developed. Examples of such imaging technology may include infrared imaging, ultra-violet imaging, thermal imaging, or any one of a variety of multi spectral imaging technologies.
In some embodiments, the present application 120 running on the electronic computing device 102 allows a user to set a desired time period and one or more geographical areas to receive the satellite images covering the geographical area(s) captured within the desired time period. The application 120 processes the received satellite images to generate the series of time-lapse images, which are further processed using the artificial intelligence-based instructions of the application 120 to identify the serviceable roofs in the particular geographical area(s) with one or more damages caused by severe weather activates or other causes that occurred in the geographical area within the time period of capturing the satellite images. In some instances, the satellite images covering the geographical area(s), captured within the desired period of time, are obtained from an aerial image capturing application launched from the electronic computing device 102. In some other instances, the present application 120 for identifying the serviceable roofs having one or more damages, within the selected geographical area(s), communicates directly with the aerial image capturing application launched from the electronic computing device 102 to generate the series of time-lapse images covering the roofs of the buildings in the selected geographical area(s). In some other instances, the aerial image capturing application launched from the electronic computing device 102 communicates with a remote satellite image data server 202 to retrieve the satellite images of the geographical area(s) captured within the selected period of time.
The instructions of the present application 120 for identifying the serviceable roofs, when executed using the processor of the electronic computing device 102, enables automated processing of the images of the roofs in the selected geographical area, which is made available in form of the series of time-lapse images from the past and real-time satellite images of the selected geographical area, to identify a variety of roof characteristics associated with each of the roofs in the images. In one or more embodiments of the disclosed principles, the roof characteristics identified by processing the images of the roofs includes a roof type, an age of the roof, at least one roof material, at least one roof dimension, at least one roof maintenance related information, at least one pre-existing roof damage related information, at least one material covering the roof, and other related roof information. In some instances, execution of the instructions of the application 120 using the processor of the electronic computing device identifies the roof characteristics of each of the roofs in the images. The application 120 identifies the roof characteristics by comparing a variety of features of the roofs identified from the series of time-lapse images of the roofs, using the artificial intelligence-based instructions of the application 120, to a number of predefined roof features associated with different roof types stored in the dynamically updated database associated with the present application 120.
The instructions of the present application 120 for identifying the serviceable roofs, when executed using the processor of the electronic computing device 102 further enables the automated retrieval of the weather data of the geographical area over the preset period of time. In some instances, the application 120 retrieves the weather data associated with the geographical area during the preset period of time from a weather data service provider. In some other instances, the application 120 retrieves the weather data associated with the geographical area during the preset period of time from a remote weather data server 204 from the weather data service provider. The instructions of the application 120, when executed using the processor associated with the electronic computing device 102, enables the automated identification of one or more weather activities in the selected geographic area, within the selected period of time, capable of damaging one or more roofs in the particular geographic area. In some instances, the weather activates capable of damaging the roofs in the particular geographic area include hailstorm activities with varying hail stone sizes rated for damaging the different types of roofs. In some instances, the artificial intelligence-based instructions of the present application 120 predicts the roofs in the particular geographical area with high chances of getting damaged after the severe weather activities such as the hailstorm activities with hail stone sizes capable of damaging the roofs. The artificial intelligence-based instructions of the present application 120 further analyzes the series of time-lapse images of the roofs before and after the severe weather activities to identify the changes in the roof characteristics associated with the roofs in the geographical area. Further, the instructions of the present application 120 for identifying the serviceable roofs, when executed using the processor of the electronic computing device 102 enables the conversion and analysis of the series of time-lapse images of the roofs through a number of image conversion steps including an image pixilation step to automatically identify one or more damages on the roofs. In some instances, the serviceable roofs within the selected geographical area with one or more damages are identified by analyzing the sequential changes in the pixels of the series of time-lapse images. These sequential changes in the pixelated images are then correlated with the roof characteristics such as the type of roof, material, age of the roof, etc., and the presence of weather activities such as hailstorm activities during or prior to the duration of the sequential changes in the pixels of the series of time-lapse images to identify the presence of any damages on the roofs. Thus, the present application 120 allows the roofing services providers to identify the serviceable roofs in the particular geographical area to sell their services and products to the right customers. Moreover, the present application 120 allows the roofing services providers to identify location information associated with each of the serviceable roofs for making direct contact with the owner of the property. Furthermore, the present application 120 allows the roofing services providers to identify the exact location of serviceable roofs in the particular geographical area, identify the roof characteristics such as the type of roof, type of roofing material, age of roof, prior maintenance activities performed on the roof, etc., and the damages to the roof, beforehand and preset the data in form of images to the owner of the buildings for easily convicting them to perform the maintenance activities on the roof. In some instances, the present application 120 allows the roofing service providers to identify the cause of the damage to the serviceable roofs, such as the weather activities including large hail stones associated with the hailstorm activities, identified in the particular geographical area and the important dates of the weather activities for assisting the building owners to contact their roofing insurance company with the provided information. The building owners can contact their roofing insurance service providers with the data provided by the roofing services provider to make an insurance claim for performing maintenance of the roof. In some other instance, the roofing service providers estimates an extent of the damage by the visual inspection of the series of time-lapse images of the roofs and provides an estimated cost of repairing or doing maintenance of the roof. In some embodiments, the application 120 for identifying the serviceable roofs provides a number of alerts and notifications to the roofing services providers regarding the serviceable roofs in a particular geographical area based on the time period of the weather activities that have caused the damages to the serviceable roofs. This Further, enables the roofing services providers to contact the owners of the buildings with the serviceable roofs within the stipulated timeframe of requesting for the roofing insurance claims. Thus, the roofing services providers can use the present application 120 to sell more roofing services and products to the right customers with a desired geographical area.
Referring now to
In some other embodiments, the information stored in the storage unit 112, for further utilization by the application 120, of the electronic computing device 102 is dynamically and automatically updated. In some other embodiments, the information stored in the storage unit 112, for further utilization by the application 120, is manually updated based on the visual verification of the images of the roofs obtained in form of the series of time-lapse images from the past and real-time satellite images of the selected geographical area. The visual inspection of the series of time-lapse images reveal a number of information related to each of the roofs such as, but not limited to, the roof material, past maintenance information of the roof, type of roof, age of the roof, past and present condition of the roof etc. The users visually analyzing the series of time-lapse images of the roofs are allowed to dynamically update the roof-related information stored in the storage unit 112. In some instances, the information related to the roof characteristics is stored in the storage unit 112 in form of a dynamically updated database 122. In addition, the weather data including the information related to the weather activities capable of damaging the different types of roofs are also stored in form of another dynamically updated database 124 within the storage unit 112. The present application 120 further allows the manual updating of both the database 122 and 124 by visually analyzing the images of the roofs presented through the display unit 108 and by analyzing the relevant weather information received through other sources. In a yet another embodiment, the instructions of the application 120 stored in the storage unit 112 includes artificial intelligence-based instructions to perform the automated processing and analysis of the images of the roofs, which is made available in the form of the series of time-lapse images from the past and present satellite images of the geographical area, and to identify the roof characteristics and the serviceable roofs with damages mainly caused by the severe weather activities. The artificial intelligence-based instructions of the application 120 when executed using the processor 106, enables automated updating of the dynamically updated database 122 for storing the identified roof characteristics, according to one or more embodiments of the disclosed principles. One or more features associated a variety of roofs types are stored in the database 122 and are automatically compared with the features of the roofs identified from the images of the roofs collected from the series of time-lapse images. The execution of the image processing instructions of the present application 120 using the processor 106 thus identifies the roof characteristics of each of the roofs and updates the relevant information into the dynamically updated database 122 storing the roof characteristics of different types of roofs. The artificial intelligence-based instructions of the application 120 enables the dynamic updating of the roof characteristics associated with each of the roofs into the dynamically updated database 122 and improves the speed and accuracy of automated identification of the roof characteristics associated with each of the roof types identified from the images. Similarly the artificial intelligence-based instructions of the present application 120, when executed using the processor 106, enables the automated identification of the weather activities, such as the magnitude of the hailstorm activities and sizes of the hail stones during the hailstorm activities, capable of damaging the different roof types. The artificial intelligence-based instructions of the application 120 analyzes the changes to the roofs prior to and after the severe weather activities and automatically updates the dynamically updated databases 124 of the weather activities stored in the storage unit 112 with the relevant information related to the severe weather activities capable of damaging the different roof types.
In some other embodiment of the disclosed principles, the electronic computing device 102, is a portable electronic device such as, but not limited to, a smartphone, tablet, laptop and other portable devices capable of executing the instructions of the application 120 for identifying the serviceable roofs in a selected geographical location. In some other embodiments, the electronic computing device 102 is any electronic device capable of launching the application, either installed into the device 102 or through a web interface. In such devices, the application is made available in form of a web application, or a software-as-a-service application, which can be accessed by the roofing services providers from anywhere for identifying the serviceable roofs in any selected geographical area. In all such instances, the application 120 running on the electronic computing devices 102, which can be a computer at the roofing services provider's location or a remote computer accessible to the roofing services provider, enables automated capturing of the images of the roofs in form of the series of time-lapse images obtained from the past and present satellite images of the geographical area, automated identification of the roofing characteristics of each of the roofs based on the features of the roofs stored in the dynamically updated database 122, identification of probable serviceable roofs in the images by correlating the identified roof characteristics of each of the roofs with the weather activities during the period of capturing the satellite images, identification of the serviceable roofs with severe damages by analyzing the sequential changes in the pixelated images of the roofs and the identification of the location information of each of the serviceable roofs with the damages.
The instructions of the present application 120 for identifying the serviceable roofs, when executed using the processor 106 of the electronic computing device 102, such as the computer provided with the roofing service provider, enables the automated analysis of each of the roofs in the images obtained in form of the time-lapse images of the roofs in the selected geographical area, which is shown in step 308. In an exemplary embodiment, the storage unit 112 of the electronic computing device 102 stores the dynamically updated database 122 of roof characteristics or roof features associated with a variety of types of roofs. The application 120 communicates with the dynamically updated database 122 of the roof characteristics to identify the types and characteristics of each of the roofs in the images as in step 316. The application 120 includes image processing instructions that identify the features, such as, but not limited to, color of the roofs, from each of the images to identify the type and the characteristics of each of the roofs in the images. As in step 314, the present application 120 identifies the similar roof features by analyzing the detected features from the images to the previously stored features from the database 122. In case the roof features are not identified from the database, the application 120 instructs the roofing services provider to manually identify the roof characteristics, as in step 320. These manually identified roof features, which are not present in the database 122 are dynamically updated by the application 120 from the user inputs related to the roof characteristics and the type of roof, which is shown in the flow diagram involving step 318.
In one or more embodiments of the disclosed principles, the image processing technique(s) performed by the processor 106, by executing the image processing instructions or the artificial intelligence-based instructions of the application, enables any suitable image detection, feature detection/extraction, pattern detection, edge detection, corner detection, blob detection, ridge detection, color detection, and/or any other image processing technique(s) to determine the roof characteristics of each of the roofs present in the series of time-lapse images obtained from the past and present satellite images of the selected geographical area(s). In some instances, the image processing instructions of the present application, when executed using the processor 106, performs a series of image processing steps, which are commonly employed to identify features from the digital image, such as, but not limited to, SIFT (Scale-Invariant Feature Transform) technique, a SURF (Speeded Up Robust Features) technique, and/or a Hough transform technique, etc., to detect the roof characteristics of each of the roofs present in the images available in form of the series of time-lapse images obtained from the past and present satellite images of the selected geographical area(s).
In some other embodiment of the disclosed principles, the image processing instructions of the present application 120, when executed using the processor 106 of the electronic computing device 102, enables identification of one or more features of the roofs and compares the identified features with the predefined or previously stored features or the roof characteristics in the dynamically updated database 122 in real-time. In some other embodiments, the image processing instructions of the application 120 include a number of artificial intelligence-based instructions configured to identify the roof characteristics, such as but not limited to, roofing material, roofing type, age of the roof, etc., by generating a matching score when comparing with the previously stored features or the roof characteristics in the dynamically updated database 122 in real-time. In a yet another embodiment, the present application 120 for identifying the serviceable roofs may incorporate a image processing and roof characteristics identification module that performs the image processing to determine which of the products or features of the roofs in the database 122 are associated with roof characteristics that “match,” or are sufficiently “similar” to, the roof characteristics of the roof determined by the present application 120. The processing steps for determining whether a particular roof characteristics in the database 122 “matches” the roof characteristic of the roofing materials present in the images may vary according to different embodiments. In some other instances, the dynamically updated database 122 storing the roofing characteristics of a variety of types of roofs may assist the application 120 to identify the roof features or the roofing characteristics of each of the roofs in the images using one or more roofing part manufacturer characteristics, such as, but not limited to, tab or tile length, recommended installation pattern, recommended exposure width, etc., associated with the roofing product. In some other instances, the dynamically updated database 122 associated with the present application may include a single database or additionally include one or more third party databases such as the respective roofing material product manufacturers.
Once the roof features of each of the roofs are identified, the present application 120 identifies the weather activities, occurred within the selected period of time, capable of damaging the identified roofs. In a certain embodiment of the disclosed principles, the weather data of the selected geographical area(s) is collected from a weather data service provider such as, but not limited to, national weather data service provider. In such an instance, the present application 120 communicates with the national weather data service provider server 204 to collect the weather data within the selected period of time. In an exemplary embodiment, the present application 120 communicates with the national oceanic and atmospheric administration servers 204 for obtaining the weather data and the received weather data map of the area within the selected period of time is overlaid on the past and present satellite images, such as, but not limited to Google Earth images, of the selected geographical area(s), captured within the same period of time. This allows the present application 120 to analyze both the images of the weather activities and the series of the time-lapse images of the roofs to identify the serviceable roofs or roofs with damages or roofs with high chances of getting damaged from the weather activities within the geographical area(s). This also enables the roofing services providers to manually identify the weather activities capable of damaging the roofs in the selected geographical area.
In some other instances, the weather data of any selected geographical area is collected from multiple weather data service provider servers 204 such as, but not limited to, www.interactivehailmaps.com, national oceanic and atmospheric administration (NOAA) and other weather data service providers. These weather data maps may include the detailed map of the hailstorm activities over the selected geographical area(s), which are analyzed by the present application in real-time to identify the possible serviceable roofs in the particular area.
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Once the serviceable roofs with the damages are identified from the pixelated images of the roofs, which are obtained from the series of time-lapse images of the roofs captured from the past and present satellite images of the geographical area, the present application 120 identifies the location information of each of these serviceable roofs with damages and transfers the information to the roofing services provider as in step 334. The roofing services providers 334 uses the location information for making contact with the owner of the building with the serviceable roof and presents the information including the images of the damages on the roof for their verification. Further, in some instances, the roofing services provider can identify the roofing insurance provider associated with the particular building and utilize the publicly available guidelines of the roofing services provider for claiming the roofing insurance and the weather activity date, which actually damaged the roof, to assist the owner of the building to request for the roofing insurance claims to perform the relevant maintenance to the roof. In some other instances, the artificial intelligence-based instructions of the application 120 when executed using the processor 106 predicts the serviceable roofs in the geographical area and a variety of roofing maintenance related information of each of the serviceable roofs with damages. These roofing maintenance related information includes at least one type of roof maintenance required, an approximate cost of maintenance, materials required for roof maintenance, a time frame for availing the roofing insurance claims and other relevant maintenance information. This allows the roofing services providers to plan and provide better roofing products and services to the customers and helps them to properly maintain the roofs of the buildings. In some embodiments, the same weather activities affect each type of roofs differently and some may cause damages and some only contributes to the change in appearance of the roofs. In some other instance, some weather activities, rated for damaging the particular roofing type only makes small defects that are not necessarily to be treated immediately, and the artificial intelligence-based instructions of the present application automatically updates the database 124 of weather activities and threshold values of each of the weather activities capable of damaging the each roof type as in steps 330 through 332. However, in some instances, the effect of the weather activities and the threshold values of each of the weather activities obtained from the database 124 may vary depending upon a previous maintenance status, age and other previous condition of the roofs prior to the selected time period for analysis. The artificial intelligence-based instructions of the present application 120 takes into account of all these factors and automatically learns and updates the database 124 for predicting the serviceable roofs and for identifying the serviceable roofs having one or more damages with higher accuracy over time.
Further, the present application analyzes the roof 506 on the right side of the image 500 to identify the roof characteristics, such as the presence of dark stains along the rear edge 508 of the roof 506, which may be caused by the collection of algae and dirt near the drains. The continuous monitoring of the dark stains along the rear edge 508 of the roof 506 from the series of time-lapse images of the roof 506 helps to identify the maintenance status, replacement or roofing material and the other relevant information of the roof 506. The present application 120 allows the automated analysis and manual inspection of the roofs present in the series of time-lapse images obtained from the past and present satellite images of the geographical area(s). This in turn improves the accuracy of the present application 120 in detecting the roof characteristics and damages on the roofs. The automated inspection of the series of time-lapse images of the roofs is performed in a number of methods as discussed earlier. However, an exemplary embodiment of the present application 120 employs one or more image pixilation steps to identify the sequential changes in each pixel of the series of time-lapse images of the roofs for accurate identification of the roof characteristics and damages on the roofs. One such exemplary method for detecting the roof characteristics and damages on the roofs is discussed below.
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The image processing instructions of the present application 120 may employ a variety of image processing techniques, some of which are disclosed below with the help of similar image processing techniques employed by several image processing prior art patent teachings. One such image processing technique employed in U.S. Pat. No. 7,711,157 titled “Artificial Intelligence Systems For Identifying Objects”. The process for object identification, according to the prior art, comprising extracting object shape features and object color features from digital images of an initial object and storing the extracted object shape features and object color features in a database, where said extracted object shape features and object color features are associated with a unique identifier associated with said object and repeating the first step for a plurality of different objects. Then, extracting object shape features and object color features from a digital image of an object whose identity is being sought and correlating the extracted object shape features and object color features of the object whose identity is being sought with the extracted object shape features and object color features previously stored in the database. If a first correlation of the extracted object shape features is better than a first threshold value for a given object associated with an identifier in the database and if a second correlation of the extracted object color features is better than a second threshold value for the given object, then making a determination that the object whose identity is being sought is said given object. In an embodiment, one or more steps of the above object identification utilizing object color, texture and shape features can be employed in the present application 120 for identifying the roof characteristics of the roofs and to identify one or more objects present on the roofs.
Another prior art utilizing artificial intelligence-based image-processing techniques, which can be incorporated into the image processing steps of the disclosed principles, is the U.S. Pat. No. 9,679,227 titled “System And Method For Detecting Features In Aerial Images Using Disparity Mapping And Segmentation Techniques”. The disclosed prior art system for aerial image detection and classification includes an aerial image database storing one or more aerial images electronically received from one or more image providers, and an object detection pre-processing engine in electronic communication with the aerial image database, the object detection pre-processing engine detecting and classifying objects using a disparity mapping generation sub-process to automatically process the one or more aerial images to generate a disparity map providing elevation information, a segmentation sub-process to automatically apply a pre-defined elevation threshold to the disparity map, the pre-defined elevation threshold adjustable by a user, and a classification sub-process to automatically detect and classify objects in the one or more stereoscopic pairs of aerial images by applying one or more automated detectors based on classification parameters and the pre-defined elevation threshold. One or more image analysis steps of the above prior art can be utilized by the present artificial intelligence-based image processing instructions of the present application 120 to identify the roof features from the images captured from the past and present satellite images.
Another prior art disclosing the image processing steps to identify the features from the images is disclosed in U.S. Pat. No. 5,625,710. The prior art recognizes the features such as the character from an image using pixelated form of the images to compare with a reference image to identify the changes in the pixels of the image from the reference image to identify the characters. A similar processing step can be used by the artificial intelligence-based image processing instructions of the present application 120 to identify the damages to the roofs by comparing with a previous image of the roof, before the damages, from the series of time-lapse images.
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Thus, the present application 120 analyzes the series of time-lapse images of the roofs and the artificial intelligence-based instructions of the application 120 continuously learns from each cycle of processing the images for providing more accurate results to the roofing services provider. In some other instances, the artificial intelligence-based instructions of the application 120 preforms automated and continuous analysis of the roofs of a particular geographical area to identify the serviceable roofs with damages and to receive real-time alerts for contacting the owners of the buildings with the serviceable roofs in time. The artificial intelligence-based instructions of the application 120 identifies the serviceable roofs by analyzing the sequential changes in the respective pixels of the series of time-lapse images and correlating with the roof characteristics and the weather activities during the series of time-lapse images capable of damaging the particular roof type. This in turn helps the owners of the building to claim their roofing insurances utilizing the visual analysis information of the roof, including images of the roof captured over a period of time, provided by the roofing services provider.
The disclosed principles further includes a computer implemented method for assisting the roofing services providers to sell a variety of roof repairing services and products to the customers, according to an exemplary embodiment of the disclosed principles.
It is noted that in the presently disclosed principles, reference is made to the roofs of buildings, however, one skilled in the art will easily understand that this method can be applied to simply inspecting the broader category of simply inspecting buildings, and not just roofs. Additionally, the present disclosure illustrates just one damaging event of concern, the impact of hail on the roofs of buildings, however, one skilled in the art will also easily understand that the disclosed principles equally apply to any type of detrimental or damaging events that involve any type of event causing damage to buildings of interest, and any type of disaster or phenomenon, such as, but not limited to any: natural disaster, manmade disaster, explosions, nuclear meltdowns, volcanoes, avalanches, land slides, weather events, hail, tornado, hurricane, typhoon, whirlwind, monsoon, cyclone, tropical storm, dam bursting, flood, fire, and earthquakes.
Further, it should be noted that the steps described in the method of use could be carried out in many different orders according to user preference. The use of “step of” should not be interpreted as “step for”, in the claims herein and is not intended to invoke the provisions of 35 U.S.C. § 112, (6). Upon reading this specification, it should be appreciated that, under appropriate circumstances, considering such issues as design preference, user preferences, marketing preferences, cost, technological advances, etc., other methods of use arrangements, elimination or addition of certain steps, including or excluding certain maintenance steps, etc., may be sufficient.
The foregoing description of the exemplary embodiments of the disclosed principles have been presented for the purpose of illustration and description. While various embodiments in accordance with the principles disclosed herein have been described above, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of this disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with any claims and their equivalents issuing from this disclosure. Furthermore, the above advantages and features are provided in described embodiments, but shall not limit the application of such issued claims to processes and structures accomplishing any or all of the above advantages.
Additionally, the section headings herein are provided for consistency with the suggestions under 37 C.F.R. 1.77 or otherwise to provide organizational cues. These headings shall not limit or characterize the invention(s) set out in any claims that may issue from this disclosure. Specifically, and by way of example, although the headings refer to a “Technical Field,” the claims should not be limited by the language chosen under this heading to describe the so-called field. Further, a description of a technology as background information is not to be construed as an admission that certain technology is prior art to any embodiment(s) in this disclosure. Neither is the “Summary” to be considered as a characterization of the embodiment(s) set forth in issued claims. Furthermore, any reference in this disclosure to “invention” in the singular should not be used to argue that there is only a single point of novelty in this disclosure. Multiple embodiments may be set forth according to the limitations of the multiple claims issuing from this disclosure, and such claims accordingly define the embodiment(s), and their equivalents, that are protected thereby. In all instances, the scope of such claims shall be considered on their own merits in light of this disclosure, but should not be constrained by the headings set forth herein.
