Capture Flood Photo Training Data via Social Scraping & Community Platform
- Organization
- Social Media Platforms (Twitter/X)
- Sector
- Research Institutions, Municipal Governments, Insurance Companies
- Location
- United States
Source reference
Executive summary
Current state
NSF awarded ISEECHANGE, Inc. $304,018 in SBIR Phase I funding to develop deep learning for flood mapping from unstructured photos, creating a commercial gap between their research capabilities and the insurance/disaster response markets that need operational tools.
Market catalyst
Massive volume of flood photos shared on social media during events represents unstructured training data that research institutions like ISEECHANGE need but cannot efficiently collect at scale, while municipalities lack real-time ground-level flood intelligence during emergencies.
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