IP Library › Granted Patent US 12,614,385
Granted Patent B2
US 12,614,385 · App. 18/198,070 · Granted Apr 28, 2026

Remote sensing and social sensing for flood mapping

Inventors: Muhammad Imran (Doha, QA); Rizwan Sadiq (Doha, QA); Zainab Akhtar (Doha, QA); Ferda Ofli (Doha, QA)
Assignee: QATAR FOUNDATION FOR EDUCATION, SCIENCE AND COMMUNITY DEVELOPMENT
G06V20/182H04N21/4318G06Q10/40
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,614,385
App. No.
18/198,070
Granted
Apr 28, 2026
Kind
B2
Abstract

Remote sensing and social sensing for flood mapping is provided by identifying where floodwater is present in an image of a location affected by a flooding event; identifying a social media post posted on a social media platform from the location and associated with the flooding event; and overlaying the image with the social media post. In some embodiments, the remote and social sensing includes one or more of: generating a permanent water mask identifying where permanent water is located at the location, and applying the permanent water mask to the image to differentiate the floodwater from permanent water for the location; identifying where the social media post was posted from; and overlaying the image with the social media post includes positioning a photograph included in the social media post for display with the image where a subject of the photograph is located at the location.

Claims (49)

1 . A method, comprising:

identifying where floodwater is present in a current image of a location affected by a flooding event, wherein identifying where floodwater is present in the current image of the location affected by the flooding event comprises:

generating a permanent water mask identifying where permanent water is located at the location; and

applying the permanent water mask to the current image to generate a differentiated image that separately indicates the floodwater from permanent water for the location;

identifying a social media post posted on a social media platform from the location and associated with the flooding event; and

overlaying the differentiated image over the social media post, wherein the social media post includes a photograph of the location, wherein overlaying the differentiated image over the social media post includes positioning the photograph for display with the differentiated image where a subject of the photograph is located at the location.

2 . The method of claim 1 , wherein identifying the social media post posted on the social media platform from the location and associated with the flooding event further comprises identifying where the social media post was posted from based on at least one of:

geolocation information included with the social media post; and

a geolocation inference based on user location data, user profile description data, and text included in the social media post.

3 . The method of claim 1 , wherein identifying the social media post posted on the social media platform from the location and associated with the flooding event further comprises filtering the social media platform for relevant social media posts, including the social media post, based on keywords and time data included in a corpus of social media posts including the relevant social media posts.

4 . The method of claim 1 , wherein social media posts from a social media service are filtered to identify the social media post by removing any social media posts that fall below or outside of any of:

a disaster type threshold;

an informativeness threshold;

a humanitarian threshold; and

a geographic threshold.

5 . The method of claim 4 , wherein the social media posts are filtered to remove duplicates and follow-on social media posts to original social media posts that do not also conform to the informativeness threshold.

6 . A system, comprising:

a processor; and

a memory, including instructions that when executed by the processor perform operations comprising:

identifying where floodwater is present in a current image of a location affected by a flooding event, wherein identifying where floodwater is present in the current image of the location affected by the flooding event comprises:

generating a permanent water mask identifying where permanent water is located at the location; and

applying the permanent water mask to the current image to generate a differentiated image that separately indicates the floodwater from permanent water for the location;

identifying a social media post posted on a social media platform from the location and associated with the flooding event; and

overlaying the differentiated image over the social media post, wherein the social media post includes a photograph of the location, wherein overlaying the differentiated image over the social media post includes positioning the photograph for display with the differentiated image where a subject of the photograph is located at the location.

7 . The system of claim 6 , wherein identifying the social media post posted on the social media platform from the location and associated with the flooding event further comprises identifying where the social media post was posted from based on at least one of:

geolocation information included with the social media post; and

a geolocation inference based on user location data, user profile description data, and text included in the social media post.

8 . The system of claim 6 , wherein identifying the social media post posted on the social media platform from the location and associated with the flooding event further comprises filtering the social media platform for relevant social media posts, including the social media post, based on keywords and time data included in a corpus of social media posts including the relevant social media posts.

9 . The system of claim 6 , wherein social media posts from a social media service are filtered to identify the social media post by removing any social media posts that fall below or outside of any of:

a disaster type threshold;

an informativeness threshold;

a humanitarian threshold; and

a geographic threshold.

10 . The system of claim 9 , wherein the social media posts are filtered to remove duplicates and follow-on social media posts to original social media posts that do not also conform to the informativeness threshold.

11 . A memory including instructions that when executed by a processor perform operations comprising:

identifying where floodwater is present in a current image of a location affected by a flooding event, wherein identifying where floodwater is present in the current image of the location affected by the flooding event comprises:

generating a permanent water mask identifying where permanent water is located at the location; and

applying the permanent water mask to the current image to generate a differentiated image that separately indicates the floodwater from permanent water for the location;

identifying a social media post posted on a social media platform from the location and associated with the flooding event; and

overlaying the differentiated image over the social media post, wherein the social media post includes a photograph of the location, wherein overlaying the differentiated image over the social media post includes positioning the photograph for display with the differentiated image where a subject of the photograph is located at the location.

12 . The memory of claim 11 , wherein identifying the social media post posted on the social media platform from the location and associated with the flooding event further comprises identifying where the social media post was posted from based on at least one of:

geolocation information included with the social media post; and

a geolocation inference based on user location data, user profile description data, and text included in the social media post.

13 . The memory of claim 11 , wherein identifying the social media post posted on the social media platform from the location and associated with the flooding event further comprises filtering the social media platform for relevant social media posts, including the social media post, based on keywords and time data included in a corpus of social media posts including the relevant social media posts.

14 . The memory of claim 11 , wherein social media posts from a social media service are filtered to identify the social media post by removing any social media posts that fall below or outside of any of:

a disaster type threshold;

an informativeness threshold;

a humanitarian threshold; and

a geographic threshold.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 23, 2024
From: IMRAN, MUHAMMAD; SADIQ, RIZWAN; AKHTAR, ZAINAB; OFLI, FERDA
To: QATAR FOUNDATION FOR EDUCATION, SCIENCE AND COMMUNITY DEVELOPMENT
Reel/Frame 066217/0290 →
Continuity (2)
Provisional Application 63364775 · May 16, 2022
Related Publication 20230368524A1 · Nov 16, 2023
References Cited (8)
US 11436777B1 · Karli · 2022 [cited by examiner]
US 20190316309A1 · Wani · 2019 [cited by examiner]
US 20200126174A1 · Halse · 2020 [cited by examiner]
Rosser, et al.; “Rapid flood inundation mapping using social media, remote sensing and topographic data”; Springerlink; Jan. 2017; (18 pages). [cited by applicant]
Huang; “Remote Sensing and Social Sensing for Improved Flood Awareness and Exposure Analysis in the Big Data Era”; University of South Carolina; 2020; (169 pages). [cited by applicant]
Cao, et al.; “Deep learning-based remote and social sensing data fusion for urban region function recognition”; ScienceDirect; vol. 163; May 2020; (9 pages). [cited by applicant]
Sadiq, et al.; “Integrating remote sensing and social sensing for flood mapping”; ScienceDirect; 2022; (16 pages). [cited by applicant]
Cervone, et al.; “Using Social Media and Satellite Data for Damage Assessment in Urban Areas During Emergencies”; Springer; 2017 (15 pages). [cited by applicant]