# US 11,907,819 B2 · Systems and methods to generate high resolution flood maps in near real time

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- Number: US 11,907,819 B2
- Document ID: US11907819B2
- Status: Granted
- Kind: B2
- Application number: 16/953,995
- Filed: 2020-11-20
- Published: 2021-05-20
- Granted: 2024-02-20
- Earliest claimed: 2019-11-20
- Assignees: University of Connecticut
- Applicants: University of Connecticut
- Inventors: Xinyi Shen; Emmanouil Anagnostou; Qing Yang
- Web page: https://patentgrep.com/patent/US11907819B2

## CPC

- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G01S 13/9027: {Pattern recognition for feature extraction}
- G06F 16/29: Geographical information databases
- G06F 16/535: Filtering based on additional data, e.g. user or group profiles
- G06N 20/00: Machine learning
- G06T 7/514: from specularities
- G06V 20/182: {Network patterns, e.g. roads or rivers}
- G06F 18/2193: {based on specific statistical tests}
- G06T 2207/10032: Satellite or aerial image; Remote sensing
- G06T 2207/20081: Training; Learning
- G06T 2207/30192: Weather; Meteorology
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A system and method to generate flood inundation maps in near real time. The system includes a plurality of computer processing modules: a flood trigger system, a SAR data query system, and a RAPID kernel algorithm system, running in real time, to identify the potential flood zones, query SAR data, and finally compute the inundation maps, respectively. As disclosed herein, the RAPID kernel algorithm is extended to a fully automated flood mapping system that requires no human interference from the initial flood events discovery to the final flood map production.

## Claims

### Claim 1

A system to generate a flood inundation map, the system comprising:
a flood trigger system configured to identify a flood occurring zone having one or more bodies of water;
a SAR data query system to identify relevant satellite images for the flood occurring zones; and
a kernel algorithm system including an electronic processor configured to
receive the data from the flood trigger system,
receive the satellite images from the SAR data query system,
generate a binary classification of water and non-water at pixel level of the satellite images,
morphologically process the satellite images to reduce over-detection of the bodies of water and to reduce under-detection of the bodies of water,
apply a multi-threshold compensation to reduce speckle noise in the bodies of water,
apply machine learning-based correction for speckle, and
generate a flood inundation map.

### Claim 2 (depends on 1)

The system of claim 1 wherein the flood inundation map is generated in near real time.

### Claim 3 (depends on 1)

The system of claim 1 wherein the electronic processor is further configured to apply a probability density threshold to identify the pixels in the satellite images correspond to water or non-water.

### Claim 4 (depends on 1)

The system of claim 1 wherein the electronic processor is further configured to generate a plurality of water masks from a single satellite image, and wherein each mask uses a different probability density threshold to reduce the over-detection and the under-detection of the bodies of water.

### Claim 5 (depends on 1)

The system of claim 1 wherein morphologically processing the satellite images includes water source tracing and improved change detection.

### Claim 6 (depends on 5)

The system of claim 5 wherein water source training includes applying a region-growing algorithm to identify water bodies from known water sources.

### Claim 7 (depends on 5)

The system of claim 5 wherein improved change detection includes applying a region-growing algorithm over the non-water pixels to identify water bodies.

### Claim 8 (depends on 1)

The system of claim 1 wherein the electronic processor is further configured to apply a correction algorithm to the satellite images to identify whether a pixel is within a standing water body or a water body in movement.

### Claim 9 (depends on 1)

The system of claim 1 wherein the flood trigger system is configured to detect fluvial flooding and pluvial flooding.

### Claim 10 (depends on 1)

The system of claim 1 wherein when the flood trigger system identifies a flood occurring zone, the SAR data query system retrieves satellite images of the flood zone on the day of flooding and a plurality of satellite images prior to the flooding.

### Claim 11

A method of generating a flood inundation map in near real time, the method comprising:
identifying, with an electronic processor, a flood event;
retrieving, with an electronic processor, a plurality of satellite images of an area defined by the flood event; and
receiving, by a kernel algorithm system, the satellite images, the kernel algorithm system configured to
apply a water identifier or a non-water identifier for each of the pixels in the satellite images,
morphologically process the satellite images to reduce over-detection of the bodies of water and to reduce under-detection of the bodies of water,
apply a multi-threshold compensation to reduce speckle noise in the bodies of water,
apply machine learning-based correction for speckle, and
generate a flood inundation map.

### Claim 12 (depends on 11)

The system of claim 11 further comprising applying a probability density threshold to identify the pixels in the satellite images that correspond to water or non-water.

### Claim 13 (depends on 11)

The system of claim 11 further comprising generating a plurality of water masks from a single satellite image, and wherein each mask uses a different probability density threshold to reduce the over-detection and the under-detection of the bodies of water.

### Claim 14 (depends on 11)

The system of claim 11 wherein morphologically processing the satellite images includes water source tracing and improved change detection.

### Claim 15 (depends on 14)

The system of claim 14 wherein water source training includes applying a region-growing algorithm to identify water bodies from known water sources.

### Claim 16 (depends on 14)

The system of claim 14 wherein improved change detection includes applying a region-growing algorithm over the non-water pixels to identify water bodies.

### Claim 17 (depends on 11)

The system of claim 11 further comprising applying a correction algorithm to the satellite images to identify whether a pixel is within a standing water body or a water body in movement.

### Claim 18 (depends on 11)

The system of claim 11 further comprising detecting fluvial flooding and pluvial flooding.