publication . Preprint . 2019

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Schmidt, Victor; Luccioni, Alexandra; Mukkavilli, S. Karthik; Balasooriya, Narmada; Sankaran, Kris; Chayes, Jennifer; Bengio, Yoshua;
Open Access English
  • Published: 02 May 2019
Abstract
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). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping that can then be applied to images of locations that have not yet experienced these events. This visual transformation is paired with climate model predictions to assess likelihood and type of climate-related events in the long term (50 years) in order to bring the future closer in the viewers mind. The eventual goal of our project...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
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29 references, page 1 of 2

Dragomir Anguelov, Carole Dulong, Daniel Filip, Christian Frueh, Ste´phane Lafon, Richard Lyon, Abhijit Ogale, Luc Vincent, and Josh Weaver. Google street view: Capturing the world at street level. Computer, 43(6):32-38, 2010. [OpenAIRE]

Adam Corner and Jamie Clarke. Talking climate: From research to practice in public engagement. Springer, 2016.

Francesco Dottori, Peter Salamon, Alessandra Bianchi, Lorenzo Alfieri, Feyera Aga Hirpa, and Luc Feyen. Development and evaluation of a framework for global flood hazard mapping. Advances in water resources, 94:87-102, 2016.

David Gianatasio. 'world under water uses streetview to visualize flooding from climate change. Adweek, 2014.

Gabriella Giannachi. Representing, performing and mitigating climate change in contemporary art practice. Leonardo, 45(2):124-131, 2012.

Yolanda Gil, Suzanne A Pierce, Hassan Babaie, Arindam Banerjee, Kirk Borne, Gary Bust, Michelle Cheatham, Imme Ebert-Uphoff, Carla Gomes, Mary Hill, et al. Intelligent systems for geosciences: an essential research agenda. Communications of the ACM, 62(1):76-84, 2018.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.

Jessica Hwang, Paulo Orenstein, Karl Pfeiffer, Judah Cohen, and Lester Mackey. Improving subseasonal forecasting in the western us with machine learning. arXiv preprint arXiv:1809.07394, 2018. [OpenAIRE]

IPCC. 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. Intergovernmental Panel on Climate Change, 2018.

Lucas N Joppa. The case for technology investments in the environment, 2017. [OpenAIRE]

Anuj Karpatne, Gowtham Atluri, James H Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, and Vipin Kumar. Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10):2318-2331, 2017. [OpenAIRE]

Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, and Vipin Kumar. Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering, 2018.

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. ICLR, 2015.

Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, et al. Exascale deep learning for climate analytics. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, pp. 51. IEEE Press, 2018. [OpenAIRE]

Amy McGovern, Kimberly L Elmore, David John Gagne, Sue Ellen Haupt, Christopher D Karstens, Ryan Lagerquist, Travis Smith, and John K Williams. Using artificial intelligence to improve realtime decision-making for high-impact weather. Bulletin of the American Meteorological Society, 98(10):2073-2090, 2017. [OpenAIRE]

29 references, page 1 of 2
Abstract
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). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping that can then be applied to images of locations that have not yet experienced these events. This visual transformation is paired with climate model predictions to assess likelihood and type of climate-related events in the long term (50 years) in order to bring the future closer in the viewers mind. The eventual goal of our project...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Download from
29 references, page 1 of 2

Dragomir Anguelov, Carole Dulong, Daniel Filip, Christian Frueh, Ste´phane Lafon, Richard Lyon, Abhijit Ogale, Luc Vincent, and Josh Weaver. Google street view: Capturing the world at street level. Computer, 43(6):32-38, 2010. [OpenAIRE]

Adam Corner and Jamie Clarke. Talking climate: From research to practice in public engagement. Springer, 2016.

Francesco Dottori, Peter Salamon, Alessandra Bianchi, Lorenzo Alfieri, Feyera Aga Hirpa, and Luc Feyen. Development and evaluation of a framework for global flood hazard mapping. Advances in water resources, 94:87-102, 2016.

David Gianatasio. 'world under water uses streetview to visualize flooding from climate change. Adweek, 2014.

Gabriella Giannachi. Representing, performing and mitigating climate change in contemporary art practice. Leonardo, 45(2):124-131, 2012.

Yolanda Gil, Suzanne A Pierce, Hassan Babaie, Arindam Banerjee, Kirk Borne, Gary Bust, Michelle Cheatham, Imme Ebert-Uphoff, Carla Gomes, Mary Hill, et al. Intelligent systems for geosciences: an essential research agenda. Communications of the ACM, 62(1):76-84, 2018.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.

Jessica Hwang, Paulo Orenstein, Karl Pfeiffer, Judah Cohen, and Lester Mackey. Improving subseasonal forecasting in the western us with machine learning. arXiv preprint arXiv:1809.07394, 2018. [OpenAIRE]

IPCC. 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. Intergovernmental Panel on Climate Change, 2018.

Lucas N Joppa. The case for technology investments in the environment, 2017. [OpenAIRE]

Anuj Karpatne, Gowtham Atluri, James H Faghmous, Michael Steinbach, Arindam Banerjee, Auroop Ganguly, Shashi Shekhar, Nagiza Samatova, and Vipin Kumar. Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10):2318-2331, 2017. [OpenAIRE]

Anuj Karpatne, Imme Ebert-Uphoff, Sai Ravela, Hassan Ali Babaie, and Vipin Kumar. Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering, 2018.

Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. ICLR, 2015.

Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, et al. Exascale deep learning for climate analytics. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, pp. 51. IEEE Press, 2018. [OpenAIRE]

Amy McGovern, Kimberly L Elmore, David John Gagne, Sue Ellen Haupt, Christopher D Karstens, Ryan Lagerquist, Travis Smith, and John K Williams. Using artificial intelligence to improve realtime decision-making for high-impact weather. Bulletin of the American Meteorological Society, 98(10):2073-2090, 2017. [OpenAIRE]

29 references, page 1 of 2
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