Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization

no code implementations • 10 Apr 2021 • Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodríguez, Océane Boulais, Aruna Sankaranarayanan, Margaux Masson-Forsythe, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava Newman

Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery.

Earth Observation Image-to-Image Translation +1

Physics-informed GANs for Coastal Flood Visualization

no code implementations • 16 Oct 2020 • Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa, Farrukh Chishtie, Natalia Díaz-Rodriguez, Océane Boulais, Aaron Piña, Dava Newman, Alexander Lavin, Yarin Gal, Chedy Raïssi

As climate change increases the intensity of natural disasters, society needs better tools for adaptation.

Interpolating GANs to Scaffold Autotelic Creativity

no code implementations • 21 Jul 2020 • Ziv Epstein, Océane Boulais, Skylar Gordon, Matt Groh

By interpolating categories generated by GANs, it is possible to create novel hybrid images.

FathomNet: An underwater image training database for ocean exploration and discovery

no code implementations • 30 Jun 2020 • Océane Boulais, Ben Woodward, Brian Schlining, Lonny Lundsten, Kevin Barnard, Katy Croff Bell, Kakani Katija

Thousands of hours of marine video data are collected annually from remotely operated vehicles (ROVs) and other underwater assets.

object-detection Object Detection