U.S. patent application number 16/585565 was filed with the patent office on 2021-04-01 for wildfire defender.
The applicant listed for this patent is The Travelers Indemnity Company. Invention is credited to Joseph Amuso, James Dykstra, John Han, Kyle J. Kelsey, George Lee, Hoa Ton-That, Stefanie M. Walker.
Application Number | 20210097850 16/585565 |
Document ID | / |
Family ID | 1000005459757 |
Filed Date | 2021-04-01 |
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United States Patent
Application |
20210097850 |
Kind Code |
A1 |
Ton-That; Hoa ; et
al. |
April 1, 2021 |
WILDFIRE DEFENDER
Abstract
A system includes a processing device and memory device storing
instructions that result in accessing a first dataset including
aerial imagery data, accessing a second dataset including property
boundary data, and identifying property boundaries associated with
a geographic area. A plurality of artificial-intelligence (AI)
models are applied to the datasets to identify and compute
information of interest. Based on the first dataset and constrained
by the property boundaries, a building detection model can be
applied to identify a building footprint, and a tree detection
model can be applied to identify one or more trees. An estimated
distance can be determined between each of the trees and a nearest
portion of the building footprint as separation data, which can be
compared to a defensible space guideline to determine a defensible
space adherence score. A wildfire risk map can be generated,
including the defensible space adherence score associated with the
geographic area.
Inventors: |
Ton-That; Hoa; (Glastonbury,
CT) ; Dykstra; James; (Manchester, CT) ; Han;
John; (Glastonbury, CT) ; Walker; Stefanie M.;
(Meriden, CT) ; Amuso; Joseph; (Stafford, CT)
; Lee; George; (North Franklin, CT) ; Kelsey; Kyle
J.; (Haddam, CT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Travelers Indemnity Company |
Hartford |
CT |
US |
|
|
Family ID: |
1000005459757 |
Appl. No.: |
16/585565 |
Filed: |
September 27, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 7/50 20170101; G06T
17/05 20130101; G06T 2207/10048 20130101; G08B 17/005 20130101;
G06K 9/00637 20130101; G06T 2207/30184 20130101; G08B 17/12
20130101; G06F 16/29 20190101; G06T 2207/30188 20130101; G08B 31/00
20130101; G06T 7/75 20170101; A01G 23/00 20130101; G06K 9/6288
20130101; G06T 2207/10032 20130101; G06T 7/0002 20130101; G06F
16/587 20190101; G06K 9/00657 20130101 |
International
Class: |
G08B 31/00 20060101
G08B031/00; G06F 16/587 20060101 G06F016/587; G06F 16/29 20060101
G06F016/29; G06K 9/00 20060101 G06K009/00; G06T 7/00 20060101
G06T007/00; G06T 7/73 20060101 G06T007/73; G06T 7/50 20060101
G06T007/50; G06K 9/62 20060101 G06K009/62; G06T 17/05 20060101
G06T017/05; G08B 17/00 20060101 G08B017/00; G08B 17/12 20060101
G08B017/12; A01G 23/00 20060101 A01G023/00 |
Claims
1. A system, comprising: a processing device; and a memory device
in communication with the processing device, the memory device
storing instructions that when executed by the processing device
result in: accessing a first dataset comprising aerial imagery data
associated with a geographic area; accessing a second dataset
comprising property boundary data associated with the geographic
area; identifying a plurality of property boundaries associated
with the geographic area; applying a building detection model to
identify a building footprint based on the first dataset and
constrained by the property boundaries; applying a tree detection
model to identify one or more trees based on the first dataset and
constrained by the property boundaries; determining an estimated
distance between each of the one or more trees and a nearest
portion of the building footprint as separation data; comparing the
separation data to a defensible space guideline to determine a
defensible space adherence score; and generating a wildfire risk
map comprising the defensible space adherence score associated with
the geographic area and constrained by the property boundaries.
2. The system of claim 1, further comprising instructions that when
executed by the processing device result in: identifying one or
more neighboring tree pairs based on a location of each of the one
or more trees; determining an estimated tree-to-tree distance for
the one or more neighboring tree pairs; and incorporating the
estimated tree-to-tree distance into the separation data.
3. The system of claim 1, further comprising instructions that when
executed by the processing device result in: identifying one or
more neighboring properties that share at least one of the property
boundaries; performing a cross-property separation analysis with
respect to the one or more neighboring properties; and
incorporating a result of the cross-property separation analysis
into the separation data.
4. The system of claim 3, wherein the cross-property separation
analysis comprises determining a shortest distance between the
building footprint and a structure on the one or more neighboring
properties.
5. The system of claim 3, wherein the cross-property separation
analysis comprises determining a shortest distance between the
building footprint and one or more trees on the one or more
neighboring properties.
6. The system of claim 3, wherein the cross-property separation
analysis comprises determining an estimated tree-to-tree distance
with respect to the one or more trees on the one or more
neighboring properties.
7. The system of claim 3, further comprising instructions that when
executed by the processing device result in: accessing a third
dataset comprising a plurality of geographic features associated
with the geographic area; and predicting a fire path spread pattern
between the one or more neighboring properties based on the
geographic features identified in the third dataset.
8. The system of claim 7, wherein the geographic features comprise
one or more of: an elevation, a body of water, and a type of ground
covering.
9. The system of claim 1, further comprising instructions that when
executed by the processing device result in: constructing a
three-dimensional model of the geographic area based on the aerial
imagery data; and performing a three-dimensional analysis based on
the three-dimensional model to determine the separation data.
10. The system of claim 9, wherein the first dataset comprises a
plurality of height data on a per-pixel basis.
11. The system of claim 9, further comprising instructions that
when executed by the processing device result in: determining a
size-based component of a wildfire risk score based on a location,
area, and height of vegetation captured in the three-dimensional
model; predicting a reduction in the wildfire risk score based on
reducing either or both of the area and height of vegetation; and
outputting a vegetation pruning recommendation with the wildfire
risk map to illustrate the predicted reduction in the wildfire risk
score by performing a size reduction of the vegetation.
12. The system of claim 1, wherein the first dataset comprises
infrared data, and further comprising instructions that when
executed by the processing device result in: identifying one or
more dead spots in the one or more trees based on the infrared
data; determining a fire risk adjustment based on the one or more
dead spots; and incorporating the fire risk adjustment into the
wildfire risk map.
13. The system of claim 12, further comprising instructions that
when executed by the processing device result in: identifying a
ground covering moisture content based on the infrared data; and
incorporating a predicted impact of the ground covering moisture
content in the wildfire risk map.
14. The system of claim 1, further comprising instructions that
when executed by the processing device result in: monitoring for a
fire event proximate to the geographic area; predicting a fire
spread path based on the fire event and the wildfire risk map; and
outputting a notification of the fire event and the fire spread
path to a user interface.
15. The system of claim 14, further comprising instructions that
when executed by the processing device result in: determining a
current weather condition and a forecast weather condition between
a location of the fire event and the geographic area; predicting a
rate of fire spreading on the fire spread path based on the current
weather condition and the forecast weather condition; predicting a
fire arrival time based on the rate of fire spreading; and
outputting the prediction of the fire arrival time with the
notification of the fire event and the fire spread path to the user
interface.
16. The system of claim 1, further comprising instructions that
when executed by the processing device result in: receiving an
update to the first dataset; comparing the update to the first
dataset with a previous version of the first dataset; identifying
one or more changes between the previous version of the first
dataset and the update to the first dataset; and modifying the
wildfire risk map based on the one or more changes.
17. A computer program product comprising a non-transitory storage
medium embodied with computer program instructions that when
executed by a computer cause the computer to implement: accessing a
first dataset comprising aerial imagery data associated with a
geographic area; accessing a second dataset comprising property
boundary data associated with the geographic area; identifying a
plurality of property boundaries associated with the geographic
area; applying a building detection model to identify a building
footprint based on the first dataset and constrained by the
property boundaries; applying a tree detection model to identify
one or more trees based on the first dataset and constrained by the
property boundaries; determining an estimated distance between each
of the one or more trees and a nearest portion of the building
footprint as separation data; comparing the separation data to a
defensible space guideline to determine a defensible space
adherence score; and generating a wildfire risk map comprising the
defensible space adherence score associated with the geographic
area and constrained by the property boundaries.
18. The computer program product of claim 17, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: identifying one or more
neighboring tree pairs based on a location of each of the one or
more trees; determining an estimated tree-to-tree distance for the
one or more neighboring tree pairs; and incorporating the estimated
tree-to-tree distance into the separation data.
19. The computer program product of claim 17, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: identifying one or more
neighboring properties that share at least one of the property
boundaries; performing a cross-property separation analysis with
respect to the one or more neighboring properties; and
incorporating a result of the cross-property separation analysis
into the separation data.
20. The computer program product of claim 19, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: accessing a third dataset
comprising a plurality of geographic features associated with the
geographic area; and predicting a fire path spread pattern between
the one or more neighboring properties based on the geographic
features identified in the third dataset.
21. The computer program product of claim 17, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: constructing a three-dimensional
model of the geographic area based on the aerial imagery data; and
performing a three-dimensional analysis based on the
three-dimensional model to determine the separation data.
22. The computer program product of claim 21, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: determining a size-based component
of a wildfire risk score based on a location, area, and height of
vegetation captured in the three-dimensional model; predicting a
reduction in the wildfire risk score based on reducing either or
both of the area and height of vegetation; and outputting a
vegetation pruning recommendation with the wildfire risk map to
illustrate the predicted reduction in the wildfire risk score by
performing a size reduction of the vegetation.
23. The computer program product of claim 17, wherein the first
dataset comprises infrared data, and further comprising computer
program instructions that when executed by the computer cause the
computer to implement: identifying one or more dead spots in the
one or more trees based on the infrared data; determining a fire
risk adjustment based on the one or more dead spots; and
incorporating the fire risk adjustment into the wildfire risk
map.
24. The computer program product of claim 23, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: identifying a ground covering
moisture content based on the infrared data; and incorporating a
predicted impact of the ground covering moisture content in the
wildfire risk map.
25. The computer program product of claim 17, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: monitoring for a fire event
proximate to the geographic area; predicting a fire spread path
based on the fire event and the wildfire risk map; and outputting a
notification of the fire event and the fire spread path to a user
interface.
26. The computer program product of claim 25, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: determining a current weather
condition and a forecast weather condition between a location of
the fire event and the geographic area; predicting a rate of fire
spreading on the fire spread path based on the current weather
condition and the forecast weather condition; predicting a fire
arrival time based on the rate of fire spreading; and outputting
the prediction of the fire arrival time with the notification of
the fire event and the fire spread path to the user interface.
27. The computer program product of claim 25, further comprising
computer program instructions that when executed by the computer
cause the computer to implement: receiving an update to the first
dataset; comparing the update to the first dataset with a previous
version of the first dataset; identifying one or more changes
between the previous version of the first dataset and the update to
the first dataset; and modifying the wildfire risk map based on the
one or more changes.
Description
BACKGROUND
[0001] 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
[0002] 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:
[0003] FIG. 1 depicts a block diagram of a system according to some
embodiments of the present invention;
[0004] FIG. 2 depicts a block diagram of a system according to some
embodiments of the present invention;
[0005] FIG. 3 depicts a data collection pattern according to some
embodiments of the present invention;
[0006] 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;
[0007] FIG. 5 depicts examples of three-dimensional models that can
be constructed from multiple datasets according to some embodiments
of the present invention;
[0008] FIG. 6 depicts a data merging process to form merged model
data according to some embodiments of the present invention;
[0009] FIG. 7 depicts a training and prediction process according
to some embodiments of the present invention;
[0010] FIG. 8 depicts a wildfire risk map according to some
embodiments of the present invention;
[0011] FIG. 9 depicts an example of geographic features that can
impact a fire path spread pattern according to some embodiments of
the present invention;
[0012] FIG. 10 depicts a remote user interface example according to
some embodiments of the present invention;
[0013] FIG. 11 depicts a user interface according to some
embodiments of the present invention;
[0014] FIG. 12 depicts a process using wildfire risk analysis for
rating and quoting according to some embodiments of the present
invention;
[0015] FIGS. 13A and 13B depict a process flow according to some
embodiments of the present invention;
[0016] FIG. 14 depicts a process flow according to some embodiments
of the present invention;
[0017] FIG. 15 depicts a process flow according to some embodiments
of the present invention;
[0018] FIG. 16 depicts a process flow according to some embodiments
of the present invention; and
[0019] FIG. 17 depicts a process flow according to some embodiments
of the present invention.
DETAILED DESCRIPTION
[0020] 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.
[0021] 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.
[0022] 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.
[0023] 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.
[0024] 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.
[0025] 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 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.
[0026] 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.
[0027] 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
[0028] 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.
[0029] 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.
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] 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.
[0039] 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.
[0040] 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.
[0041] 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.
[0042] 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 512. 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. A three-dimensional model 508 of the vegetation 408
of FIG. 4, such as a tree, can enable a determination of the
approximate height, area, 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.
[0043] 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.
[0044] 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 tree 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 tree detection model 712 can identify a
tree position, a tree outline, type, health, and other such
features with seasonal adjustments, such as summer condition versus
winter condition. Further, the building detection model 710 and
tree 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, tree health can change a fire
risk along with the presence of leaves/needles and moisture
content.
[0045] 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 tree 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 tree canopies
through a graphical user interface and label the tree canopies.
Examples can be selected for many tree 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. The training process 702 can train the tree 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 tree detection based
on image data from the aerial imagery data 119. The tree 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 tree species. Point-cloud data can be used
to identify the height of trees and shrubs and may assist in
identifying other characteristics, such as tracking rate of growth
over a period of time.
[0046] 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.
[0047] 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, an unknown structure, and/or a
property contents. The results of model predictions 722 can be
further conditioned by result postprocessing 724.
[0048] 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 trees and a 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 >=a
confidence threshold) using the building detection model 710 or
tree 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.
[0049] 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.
[0050] 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 804A, 804B, 804C, and
804D for properties 805A, 805B, 805C, and 805D. 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. Separation distances between trees 806 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 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 808A, 808B, 808C, 808D associated
with each of the properties 805A, 805B, 805C, 805D. The wildfire
risk map 800 can be generated with the defensible space adherence
scores 808A, 808B, 808C, 8080D 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 805A-805D based on geographic features in
combination with building footprints 804A-804D, trees 806, and
other such data.
[0051] 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.
[0052] 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
805A-805D of FIG. 8.
[0053] 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. 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.
[0054] 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. 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).
[0055] 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.
[0056] At step 1302, the data processing server 102 can access a
first dataset including aerial imagery data 119 associated with a
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.
[0057] 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 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.
[0058] At step 1310, the data processing server 102 can apply a
tree detection model 712 to identify one or more trees 806 based on
the first dataset and constrained by the property boundaries 802,
1106. At step 1312, the data processing server 102 can determine an
estimated distance between each of the one or more trees 806 and a
nearest portion of the building footprint 804 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. At step 1316, the data
processing server 102 can generate a wildfire risk map 800,
including the defensible space adherence score 808A-808D associated
with the geographic area and constrained by the property boundaries
802, 1106. 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.
[0059] The data processing server 102 can also create a record
including an indicator of the geographic area, the property
805A-805D, and the defensible space adherence score 808A-808D. 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 805A-805D, for instance, as part of process 1200.
[0060] 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.
[0061] 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.
[0062] In reference to process 1400, at step 1402, the data
processing server 102 can identify one or more neighboring
properties 805A-805D 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 805A-805D. 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 805A-805D, 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 805A-805D.
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 805A-805D. At
step 1406, the data processing server 102 can incorporate a result
of the cross-property separation analysis into the separation data
725.
[0063] 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 805A-805D based on the geographic features 900
identified in the third dataset. For instance, a chain of
properties 805A-805D 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.
[0064] 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 804A-804D
of a building 402 or house 406 may result in an anticipated
reduction in the wildfire score for the associated property
805A-805D. 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.
[0065] 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 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseb and 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.
[0071] 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's computer, partly on the user's computer, as a stand-alone
software package, partly on the user's computer and partly on a
remote computer, or entirely on the remote computer or server.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] "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.
[0080] 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.
[0081] A "processor" generally means any one or more
microprocessors, CPU devices, computing devices, microcontrollers,
digital signal processors, or like devices, as further described
herein.
[0082] 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.
[0083] 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.
[0084] 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.TM., TDMA, CDMA,
3G.
[0085] 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.
[0086] 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.
* * * * *