52
0
0
Like?
Please wait...
About This Project
Flooding affects ~1.81 billion people worldwide, rising with urban growth. Flood patterns and impacts differ across locations due to varying geography and severity. Deucalion and Pyrrha datasets enable AI to detect flooded areas, wet surfaces, damages, vehicles and people in photos. Expanding them with geographically diverse images is expected to improve AI accuracy and effectiveness, enhancing disaster response by sourcing from thousands of social media posts and protecting lives and property.
More Lab Notes From This Project

Browse Other Projects on Experiment
Related Projects
Expanding flood image datasets for AI training and improved disaster response
Flooding affects ~1.81 billion people worldwide, rising with urban growth. Flood patterns and impacts differ...
Study of oceanographic and meteorological variables in MPA through remote sensing.
Climate change and events that modify the global temperature and precipitation dynamics such as “El Niño...
Investigating the roles of microbes in biodegrading or colonizing microplastic surfaces
Recently, we observed different PCB adsorption onto microplastic surfaces in Newtown Creek compared to Navy...

