NASA · NASA-8 · record R0106

Deep Learning for Flood Mapping (DELTA)

National Aeronautics and Space Administration · ARC: Ames Research Center

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Overview

Development stage: RetiredSource: 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.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: Image and video analysis Derived by keyword rules; see methodology

Problem the AI is intended to solve

DELTA simplifies machine learning for satellite imagery.

Expected benefits

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.

System outputs

monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle

Technology

AI classification: Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported