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We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).
An iconic approach for representing climate change
Saffron J O’Neill and Mike Hulme · 2009
Earlier work this paper cites.
Google street view: Capturing the world at street level
Dragomir Anguelov, Carole Dulong, Daniel Filip, Christian Frueh, Stéphane Lafon, Richard Lyon, Abhijit Ogale, Luc Vincent, and Josh Weaver · 2010
Earlier work this paper cites.
Public understanding of climate change in the united states
Elke U Weber and Paul C Stern · 2011
Earlier work this paper cites.
Representing, performing and mitigating climate change in contemporary art practice
Gabriella Giannachi · 2012
Earlier work this paper cites.
Public understanding of, and attitudes to, climate change: Uk and international perspectives and policy
Nick Pidgeon · 2012
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Visualizing climate change: a guide to visual communication of climate change and developing local solutions
Stephen RJ Sheppard · 2012
Earlier work this paper cites.
Climate informatics: accelerating discovering in climate science with machine learning
Claire Monteleoni, Gavin A Schmidt, and Scott McQuade · 2013
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‘world under water’ uses streetview to visualize flooding from climate change
David Gianatasio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Talking climate: From research to practice in public engagement
Adam Corner and Jamie Clarke · 2016
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Development and evaluation of a framework for global flood hazard mapping
Francesco Dottori, Peter Salamon, Alessandra Bianchi, Lorenzo Alfieri, Feyera Aga Hirpa, and Luc Feyen · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Why the human brain ignores climate change - and what to do about it
Espen Stoknes · 2016
Cited alongside, same era.
The case for technology investments in the environment, 2017
Lucas N Joppa · 2017
Cited alongside, same era.
Theory-guided data science: A new paradigm for scientific discovery from data
Anuj Karpatne, Gowtham Atluri, James H Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, and Vipin Kumar · 2017
Cited alongside, same era.
Using artificial intelligence to improve real-time decision-making for high-impact weather
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Intelligent systems for geosciences: an essential research agenda
Yolanda Gil, Suzanne A Pierce, Hassan Babaie, Arindam Banerjee, Kirk Borne, Gary Bust, Michelle Cheatham, Imme Ebert-Uphoff, Carla Gomes, Mary Hill, et al · 2018
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Improving subseasonal forecasting in the western us with machine learning
Jessica Hwang, Paulo Orenstein, Karl Pfeiffer, Judah Cohen, and Lester Mackey · 2018
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Global Warming of 1.5° C: An IPCC Special Report on the Impacts of Global Warming of 1.5° C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty
IPCC · 2018
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Amy McGovern, Kimberly L Elmore, David John Gagne, Sue Ellen Haupt, Christopher D Karstens, Ryan Lagerquist, Travis Smith, and John K Williams · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Mr Prabhat, and Chris Pal · 2017
Cited alongside, same era.
A physics-based approach to unsupervised discovery of coherent structures in spatiotemporal systems
Adam Rupe, James P Crutchfield, Karthik Kashinath, et al · 2017
Cited alongside, same era.
Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations
Tapio Schneider, Shiwei Lan, Andrew Stuart, and João Teixeira · 2017
Cited alongside, same era.
Machine learning for the geosciences: Challenges and opportunities
Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, and Vipin Kumar · 2018
Later among the works it cites.
Exascale deep learning for climate analytics
Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, et al · 2018
Later among the works it cites.
Deep learning to represent subgrid processes in climate models
Stephan Rasp, Michael S Pritchard, and Pierre Gentine · 2018
Later among the works it cites.
Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard
Michalis I Vousdoukas, Lorenzo Mentaschi, Evangelos Voukouvalas, Martin Verlaan, Svetlana Jevrejeva, Luke P Jackson, and Luc Feyen · 2018
Later among the works it cites.
Current fossil fuel infrastructure does not yet commit us to 1.5 c warming
Christopher J Smith, Piers M Forster, Myles Allen, Jan Fuglestvedt, Richard J Millar, Joeri Rogelj, and Kirsten Zickfeld · 2019
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