Sbir phase i: deep learning framework for mapping flood extent from unstructured photos

The National Science Foundation awarded Iseechange Inc. $304,018 in project grant funding on July 15, 2026, under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to develop a deep learning framework that automatically converts unstructured flood photographs into precise, georeferenced flood extent maps using advanced computer vision and artificial intelligence.

The core technical innovation addresses the automated identification and matching of ground control points between individual smartphone images and geographic maps—a process historically requiring manual intervention. The Phase I research focuses on overcoming the primary barrier: automatically detecting stable reference features within unstructured, potentially noise-obscured or water-obscured images and aligning them to existing geospatial data. The scope includes automating image segmentation for flooded areas and developing deep learning models to detect visual features that enable accurate flood extent mapping.

The delivered output is a scalable system that converts real-time community-generated smartphone images and videos into near-real-time flood maps for emergency responders, infrastructure planners, utilities, and insurance companies. This addresses current limitations in flood response where remote sensors and satellites lack sufficient resolution and field surveys prove too expensive.

Iseechange Inc., a minority-owned, woman-owned small business based in New Orleans, Louisiana, is the recipient. The period of performance runs from the award date through December 31, 2026.

Abstract

The broader/ commercial impact of this small business innovation research (sbir) phase i project is in helping communities handle floods better by turning public photos and videos into near-real-time useful data. Currently, IT is difficult to get a clear picture during emergencies because sensors and satellites are often too far away or field surveys are too expensive. This system uses smartphone images to create real-time maps that help rescuers and city planners see exactly where the water is. This technology makes flood response faster and more accurate, which helps save lives and reduce property damage. For businesses, this tool provides a scalable service for emergency responders, infrastructure planners, utilities, and insurance companies, creating new jobs in the tech industry. Overall, this project creates a more affordable and effective way for the nation to manage flood risks using the photos people already take on their phones. The core technical innovation of this project is a high-risk, difficult-to-replicate method for automatically converting individual flood photographs into precise, georeferenced flood extent maps using artificial intelligence. This effort utilizes advanced computer vision to identify and match ground control points between unstructured images and geographic maps, a process that has historically required time-consuming manual intervention. This innovation is particularly challenging because IT requires identifying stable reference features in unstructured images that may be noisy or partially obscured by floodwater. The phase i research focuses on overcoming the primary technical barrier: automatically identifying reference features in unstructured images and matching them to existing geospatial data. The research scope includes automating image segmentation for flooded areas, developing and testing deep learning models that detect visual features and align them with mapped reference points, and leveraging monoplotting techniques to project observed flood boundaries onto elevation maps. The resulting localized flood extents will then be combined to form continuous maps of peak inundation at the storm level. The work will demonstrate technical feasibility, quantify accuracy, and reduce uncertainty in an automated workflow suitable for operational use. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.