SYSTEM FOR IDENTIFYING DAMAGED BUILDINGS

• US 20200134573A1
• Filed: 10/31/2018
• Published: 04/30/2020
• Est. Priority Date: 10/31/2018
• Status: Abandoned Application

• Current Assignee: Alexander Vickers
• Original Assignee: Alexander Vickers
• Inventors: Vickers, Alexander
• Application Number:
  US16/176,858
• Publication Number:
  US 20200134573A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 50/08 Construction
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/20061 Hough transform
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

A system for identifying buildings that are damaged in a geographic area from a damaging weather event. Accessing weather data and identifying a date of the damaging weather event. Accessing geographic data for identifying the geographic area where the damaging weather event occurred. Accessing visual data of buildings where the damaging weather event occurred. Identifying an individual building that was damaged based on the visual data, geographic data and weather data.

Claims

1. A system for assisting at least one roofing services provider to sell a plurality of roof repairing services and products to a plurality of customers, comprising:

an electronic computing device having a memory unit configured to store a plurality of instructions of an application for identifying a plurality of serviceable roofs in a geographical area and a processor configured to execute the plurality of instructions of the application to perform a plurality of tasks including;

a) capturing a plurality of images of a plurality of roofs in the geographical area,

wherein the plurality of images of the roofs being a series of time-lapse images, obtained from a plurality of past and real-time satellite images of the geographical area, captured over a preset period of time;

b) processing the plurality of images of the roofs using a plurality of artificial intelligence (AI) based instructions to identify a plurality of roof characteristics,

wherein the plurality of roof characteristics is identified by comparing a plurality of features identified from the series of time-lapse images of the roofs with a plurality of predefined roof features associated with a plurality of roof types stored in a dynamically updated database;

c) obtaining a plurality of weather data of the geographical area over the preset period of time from at least one weather data service provider,

wherein the weather data include a plurality of weather activities including a plurality of hailstorm activities capable of damaging the plurality of roof types;

d) converting the series of time-lapse images of the roofs through a plurality of image conversion steps including an image pixilation step to identify a plurality of damages on the roofs,

wherein the plurality of damages on the roofs forming the plurality of serviceable roofs being identified by analyzing a plurality of sequential changes in a plurality of pixels of the series of time-lapse images and correlating with the roof characteristics and the plurality of weather activities during the series of time-lapse images capable of damaging the roof type; and

e) collecting at least one location information associated with a property having the serviceable roof from the plurality of past and real-time satellite images,

whereby the real-time satellite images of the geographical area presented through the real-time satellite image presentation application is captured using the application running on the electronic computing device and retrievably stores in a storage unit for processing using the processor to identify the plurality of serviceable roofs in the geographical area.

2. The system of claim 1, wherein the plurality of roof characteristics is identified by comparing the plurality of features identified from the series of time-lapse images of the roofs to the plurality predefined roof features associated with the roof types stored in the dynamically updated database;

wherein, the plurality of roof characteristics identified from the series of time-lapse images include 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.

3. The system of claim 1, wherein the plurality of damages on the roofs is identified by comparing the plurality of sequential changes in the plurality of pixels of the series of time-lapse images, a plurality changes in the roof characteristics from the series of time-lapse images and correlating with the weather activities capable of damaging the roof type during the series of time-lapse images;

wherein, the plurality of weather activities include the hailstorm activities, heavy rain, wind, storm, lightning and other weather related activities capable of damaging the roof type.

4. The system of claim 1, wherein the artificial intelligence-based instructions of the application when executed using the processor predicts the plurality of serviceable roofs in the geographical area and a plurality of roofing maintenance related information;

wherein, the 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.

5. The system of claim 1, wherein the images are generated using multispectral imaging technology selected from the group consisting of infrared, ultra-violet and thermal imaging.

6. A computer implemented method for assisting at least one roofing services provider to sell a plurality of roof repairing services and products to a plurality of customers, comprising the steps of:

a) providing the roofing service provider with an application configured to run on an electronic computing device for identifying a plurality of serviceable roofs in a geographical area;

b) launching the application using the electronic computing device to capture a plurality of images of a plurality of roofs in the geographical area;

c) selecting a desired time period for capturing the plurality of images of the plurality of roofs in the geographical area,

wherein the plurality of images of the roofs being a series of time-lapse images, obtained from a plurality of past and real-time satellite images of the geographical area, captured over the desired time period;

d) identifying a plurality of roof characteristics of the plurality of roofs in the geographical area by analyzing the plurality of images;

e) receiving a plurality of weather data including a plurality of weather activities capable of damaging a plurality of roof types during the desired time period;

f) enabling automated conversion of the series of time-lapse images through a plurality of image conversion steps including an image pixilation step to identify a plurality of damages associated with the plurality of serviceable roofs; and

g) collecting at least one location information associated with a plurality of properties having the serviceable roofs.

7. The method of claim 6, wherein the application enables an automated and a manual analysis of the plurality of images presented in form of the series of time-lapse images of the roofs to identify the plurality of roof characteristics.

8. The method of claim 7, wherein the automated analysis of the series of time-lapse images of the roofs, captured over the desired time period, for identifying the plurality of roof characteristics is performed based on a plurality of artificial intelligence-based instructions of the application,

wherein the plurality of artificial intelligence-based instructions of the application when executed using a processor of the electronic computing device enables identification of at least one 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.

9. The method of claim 6, wherein the plurality of damages on the roofs is identified by analyzing a plurality of sequential changes in a plurality of pixels of the series of time-lapse images and a plurality of changes in the roof characteristics and correlating with the plurality of weather activities capable of damaging the roof type during the series of time-lapse images.

10. The method of claim 6, wherein the artificial intelligence-based instructions of the application when executed using the processor enables prediction of a plurality of roofing maintenance related information for the serviceable roofs;

wherein, the 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.

11. The method of claim 6, wherein the location information associated with the plurality of properties having the serviceable roofs is identified from the plurality of past and real-time satellite images.

12. A system for identifying buildings that are damaged in a geographic area from a damaging event, comprising:

a) accessing a date of the damaging event;

b) accessing geographic data for identifying a geographic area where the damaging event occurred;

c) accessing visual data about buildings where the damaging event occurred; and

d) identifying an individual building that was damaged based on the visual data, geographic data.

13. The method of claim 12, wherein the damaging event is selected from a group consisting of:

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.

14. The method of claim 12, further including accessing weather data and dates associated thereto.

15. The method of claim 12, wherein accessing the visual data involves use of global positioning systems or services that allow for identification of addresses of buildings and display of a building.

16. The method of claim 12, wherein the visual data involves visual images of roofs of buildings.

17. The method of claim 16, wherein the visual images are generated using multispectral imaging technology selected from the group consisting of infrared, ultra-violet and thermal imaging.

18. The method of claim 12, wherein accessing the visual data involves use of geographic mapping system that allows for identification of addresses of buildings.

19. The method of claim 14, wherein the weather data is at least in part derived from NOAA (National Oceanic and Atmospheric Administration) collected data.

20. The method of claim 12, wherein accessing the visual data of the buildings that were in the damaging event is examined before and after the date of the damaging event.