Deep Learning for Flood Mapping (DELTA)
Description
DELTA simplifies machine learning for satellite imagery.
Detailed example
monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
d) Retired – The use case was reported in the agency’s prior year’s inventory, but its development and/or use has since been discontinued.
Expected benefit
Remotely sensed imagery is increasingly used by emergency managers to monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle. To reduce latency in delivery of imagery-derived information, ensure consistent and reliably derived map products, and facilitate processing of an increasing volume of remote sensed data-streams, automated flood mapping workflows are needed. A joint USGS-NASA-Univ. Alabama initiative developed DELTA and applied it to automatic near-real time flood detection, using multiple sources of satellite imagery for use in disaster response.
Controls / human review
ATO: Not reported; PIA: Not published