Flood Mapping with Social Media Overlay and Water Masking
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Solution Overview
Problem
Existing remote sensing methods for flood mapping fail to provide critical contextual information about structural damage or urgent needs of affected populations, as they rely solely on satellite imagery.
Innovation Solution
Integration of remote sensing data with social sensing data from eyewitnesses and affected individuals using deep learning models to process heterogeneous data, combining satellite images with social media posts to derive informed flood extent maps that include confirmatory, complementary, and novel information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote sensing through satellite imagery is used to identify flooded areas, then flood extent can be monitored effectively, but critical contextual information about structural damage and urgent needs of affected population cannot be obtained
Solution Approach 1:
The patent combines remote sensing satellite imagery with social sensing data from online social networking platforms to create a comprehensive flood assessment system. This merging of data sources allows simultaneous acquisition of flood extent information (from satellite imagery) and contextual information (from social media posts about structural damage and urgent needs), resolving the contradiction between monitoring precision and information completeness
Solution Approach 2:
The integrated system serves multiple functions: it identifies flood extent through satellite imagery, captures contextual information about structural damage and urgent needs through social media analysis, and provides comprehensive flood assessment for disaster response. This multi-functionality eliminates the limitation of single-purpose remote sensing systems
2Loss of information
If social media data is integrated with satellite imagery, then contextual information is enriched, but data processing complexity increases
Solution Approach 1:
The patent employs deep learning models as intermediary components that automatically process and integrate heterogeneous data from satellite imagery and social media platforms. These intermediary systems handle the complexity of data fusion, extracting relevant features and combining them into a unified flood assessment framework, thereby managing processing complexity while enriching contextual information
Data Source
AI summary
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.