# US 12,005,281 B2 · Fire monitoring

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,005,281 B2
- Document ID: US12005281B2
- Status: Granted
- Kind: B2
- Application number: 17/309,350
- Filed: 2019-11-19
- Published: 2022-01-20
- Granted: 2024-06-11
- Earliest claimed: 2018-11-21
- Assignees: One Concern, Inc.
- Applicants: Ali Tohidi; Nicholas McCarthy; Yawar Aziz; Nicole Hu; Ahmad Wani; Timothy Frank
- Inventors: Ali Tohidi; Nicholas McCarthy; Yawar Aziz; Nicole Hu; Ahmad Wani; Timothy Frank
- Web page: https://patentgrep.com/patent/US12005281B2

## CPC

- A62C 3/0271: {Detection of area conflagration fires (fire alarms for forest fires G08B17/005)}
- G01W 1/10: Devices for predicting weather conditions (computers per se G06; display devices G09)
- G06F 16/587: using geographical or spatial information, e.g. location
- G06F 16/9038: Presentation of query results
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G08B 17/005: {for forest fires, e.g. detecting fires spread over a large or outdoors area (fire fighting forest fires A62C3/02)}
- G08B 31/00: Predictive alarm systems characterised by extrapolation or other computation using updated historic data
- A62C 3/02: for area conflagrations, e.g. forest fires, subterranean fires
- G06N 3/08: Learning methods
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- A62C: FIRE-FIGHTING (fire-extinguishing compositions, use of chemical substances in extinguishing fires A62D1/00; spraying, applying liquids or other fluent materials to surfaces in general B05; alarm arrangements G08B, e.g. fire alarms actuated by smoke or gases G08B17/10)
- G01W: METEOROLOGY (radar, sonar, lidar or analogous systems, designed for meteorological use G01S13/95, G01S15/88, G01S17/95)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS

## Abstract

Tools for fire monitoring are presented. One method includes an operation for accessing values of features for monitoring a fire in a region. The features include satellite images at a first resolution, vegetation information, and weather data. Further, each satellite image includes first cells associated with the geographical region and the first resolution defines a first size of each first cell. The method further includes generating a map of the geographical region comprising a plurality of second cells having a second size, which is smaller than the first size. Additionally, the method includes operations for estimating, using a machine-learning model, probability values for the second cells in the map based on the features, each probability value indicating if the second cell contains an active fire, and for updating the map based on the probability values for the second cells. The map is presented in a user interface.

## Claims

### Claim 1

A method comprising:
accessing, by a computer system, a database to obtain values for a plurality of features associated with a fire in a geographical region, the plurality of features comprising one or more satellite images at a first resolution, each satellite image comprising a plurality of first cells associated with the geographical region, the first resolution defining a first size of each first cell;
generating a map of the geographical region, the map comprising a plurality of second cells having a second size, the second size being smaller than the first size, the map of the geographical region having a higher resolution than a resolution of the one or more satellite images;
estimating, using a machine-learning model, probability values for the second cells in the map based on the plurality of features, each probability value indicating if the second cell contains an active fire;
updating the map of the geographical region based on the probability values for the second cells; and
causing presentation of the map in a user interface.

### Claim 2 (depends on 1)

The method of claim 1, comprising:
before estimating the probability values, training the machine-learning model with values of the plurality of features from previous fires.

### Claim 3 (depends on 1)

The method of claim 1, wherein the plurality of features comprises:
vegetation information for the geographical region;
weather data for the geographical region; and
land-related features that include land use, slope, aspect, elevation, and soil moisture.

### Claim 4 (depends on 3)

The method of claim 3, wherein the vegetation information comprises canopy height, canopy cover, canopy bulk density, and drought index; wherein the weather data comprises wind, precipitation, temperature, humidity, and cloud cover.

### Claim 5 (depends on 1)

The method of claim 1, wherein the plurality of features comprises hour of the day, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI).

### Claim 6 (depends on 1)

The method of claim 1, comprising:
estimating, by the machine-learning model, second cells with embers that may ignite fires; and
presenting, in the map, an ember area with second cells that comprise embers that may ignite fires.

### Claim 7 (depends on 1)

The method of claim 1, comprising:
estimating a fire scar area with second cells that have already burnt; and
causing presentation in the user interface of the map with the fire scar area.

### Claim 8 (depends on 1)

The method of claim 1, wherein the one or more satellite images comprise images in a plurality of frequency bands.

### Claim 9 (depends on 1)

The method of claim 1, wherein the first size is a square with 375 meter sides and the second size is a square with 30 meter sides.

### Claim 10 (depends on 1)

The method of claim 1, comprising:
receiving observations about a state of the fire in the geographical region; and
updating the probability values for the second cells based on the received observations.

### Claim 11

A system comprising:
a memory comprising instructions; and
one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations comprising:
accessing, by a computer system, a database to obtain values for a plurality of features associated with a fire in a geographical region, the plurality of features comprising one or more satellite images at a first resolution, each satellite image comprising a plurality of first cells associated with the geographical region, the first resolution defining a first size of each first cell;
generating a map of the geographical region, the map comprising a plurality of second cells having a second size, the second size being smaller than the first size, the map of the geographical region having a higher resolution than a resolution of the one or more satellite images;
estimating, using a machine-learning model, probability values for the second cells in the map based on the plurality of features, each probability value indicating if the second cell contains an active fire;
updating the map of the geographical region based on the probability values for the second cells; and
causing presentation of the map in a user interface.

### Claim 12 (depends on 11)

The system of claim 11, wherein the instructions cause the one or more computer processors to perform operations comprising:
before estimating the probability values, training the machine-learning model, with values of the plurality of features from previous fires.

### Claim 13 (depends on 11)

The system of claim 11, wherein the plurality of features comprises:
vegetation information for the geographical region;
weather data for the geographical region;
land-related features that include land use, slope, aspect, elevation, and soil moisture; and
hour of the day, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI).

### Claim 14 (depends on 13)

The system of claim 13, wherein the vegetation information comprises canopy height, canopy cover, canopy bulk density, and drought index; wherein the weather data comprises wind, precipitation, temperature, humidity, and cloud cover.

### Claim 15 (depends on 11)

The system of claim 11, wherein the instructions cause the one or more computer processors to perform operations comprising:
estimating, by the machine-learning model, second cells with embers that may ignite fires; and
presenting, in the map, an ember area with second cells with embers that may ignite fires.

### Claim 16

A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:
accessing, by a computer system, a database to obtain values for a plurality of features associated with a fire in a geographical region, the plurality of features comprising one or more satellite images at a first resolution, each satellite image comprising a plurality of first cells associated with the geographical region, the first resolution defining a first size of each first cell;
generating a map of the geographical region, the map comprising a plurality of second cells having a second size, the second size being smaller than the first size, the map of the geographical region having a higher resolution than a resolution of the one or more satellite images;
estimating, using a machine-learning model, probability values for the second cells in the map based on the plurality of features, each probability value indicating if the second cell contains an active fire;
updating the map of the geographical region based on the probability values for the second cells; and
causing presentation of the map in a user interface.

### Claim 17 (depends on 16)

The non-transitory machine-readable storage medium of claim 16, wherein the machine performs operations comprising:
before estimating the probability values, training the machine-learning model, with values of the plurality of features from previous fires.

### Claim 18 (depends on 16)

The non-transitory machine-readable storage medium of claim 16, wherein the plurality of features comprises:
vegetation information for the geographical region;
weather data for the geographical region;
land-related features that include land use, slope, aspect, elevation, and soil moisture; and
hour of the day, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI).

### Claim 19 (depends on 18)

The non-transitory machine-readable storage medium of claim 18, wherein the vegetation information comprises canopy height, canopy cover, canopy bulk density, and drought index; wherein the weather data comprises wind, precipitation, temperature, humidity, and cloud cover.

### Claim 20 (depends on 16)

The non-transitory machine-readable storage medium of claim 16, wherein the machine performs operations comprising:
estimating, by the machine-learning model, second cells with embers that may ignite fires; and
presenting, in the map, an ember area with second cells with embers that may ignite fires.