SXSW 2020
AI Against Disasters: Data-Driven Relief
Description:
In September 2018, Hurricane Florence devastated the eastern U.S., forcing more than 1.7 million people to evacuate their homes. Immediately after the storm, federal relief agencies started collecting imagery to identify flooded areas and damaged infrastructure to rescue, relieve, and rebuild affected communities, but current analysis methods can be slow and inefficient. The Department of Defense’s Joint Artificial Intelligence Center (JAIC) and the Johns Hopkins Applied Physics Laboratory (APL) teamed to quickly build AI-enabled capabilities that accelerated that analysis; the results immediately increased the effectiveness of relief efforts. Learn how the JAIC/APL team used deep learning algorithms in this work, and preview upcoming tech and new AI-based disaster response tools.
Other Resources / Information
Takeaways
- How does AI/deep learning accurately identify flooded areas and damage from imagery?
- How does AI augment and assist humans in making flood disaster and damage assessments?
- How else can AI be used in planning and in response to natural disasters?
Speakers
- Beatrice Garcia, Software Engineer/Project Manager, Johns Hopkins Applied Physics Laboratory
- Dominic Garcia, Humanitarian Assistance Disaster Relief Project Manager, Dept. of Defense Joint Artificial Intelligence Center
- Angeline Aguinaldo, Computer Science Researcher, Johns Hopkins Applied Physics Laboratory
Organizer
Gina Ellrich, External Communications, The Johns Hopkins University Applied Physics Laboratory
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