Using Artificial Intelligence to Visualize the Impacts of Climate Change

Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment. One reason for this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract. To overcome these challenges, we propose to use cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer. It will allow a user to enter an address—be it their house, their school, or their workplace—-and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires. This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.

[1]  Yoshua Bengio,et al.  Tackling Climate Change with Machine Learning , 2019, ACM Comput. Surv..

[2]  Kwang In Kim,et al.  Unsupervised Attention-guided Image to Image Translation , 2018, NeurIPS.

[3]  Simon A. Levin,et al.  The tragedy of cognition: psychological biases and environmental inaction , 2009 .

[4]  Rob Fergus,et al.  Depth Map Prediction from a Single Image using a Multi-Scale Deep Network , 2014, NIPS.

[5]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[6]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Taesung Park,et al.  Semantic Image Synthesis With Spatially-Adaptive Normalization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Anthony Leiserowitz,et al.  Improving Public Engagement With Climate Change , 2015, Perspectives on psychological science : a journal of the Association for Psychological Science.

[9]  N. Nakicenovic,et al.  RCP 8.5—A scenario of comparatively high greenhouse gas emissions , 2011 .

[10]  Timothy B. Gravelle,et al.  The Distribution of Climate Change Public Opinion in Canada , 2016, PloS one.

[11]  Jan Kautz,et al.  Multimodal Unsupervised Image-to-Image Translation , 2018, ECCV.

[12]  Yoshua Bengio,et al.  On the Morality of Artificial Intelligence [Commentary] , 2020, IEEE Technol. Soc. Mag..

[13]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[14]  Hal E. Hershfield,et al.  Increasing Saving Behavior Through Age-Progressed Renderings of the Future Self , 2011, JMR, Journal of marketing research.

[15]  M. Fitzpatrick,et al.  Contemporary climatic analogs for 540 North American urban areas in the late 21st century , 2019, Nature Communications.

[16]  Hal E. Hershfield,et al.  The Illusion of Wealth and Its Reversal , 2014 .

[17]  Adam Corner,et al.  Climate visuals: A mixed methods investigation of public perceptions of climate images in three countries , 2016 .

[18]  Dumitru Erhan,et al.  Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  A. Corner,et al.  Public engagement with climate imagery in a changing digital landscape , 2018 .

[20]  Aleksandra Dulic,et al.  Future delta 2.0 an experiential learning context for a serious game about local climate change , 2015, SIGGRAPH Asia Symposium on Education.

[21]  S. Sheppard Visualizing Climate Change: A Guide to Visual Communication of Climate Change and Developing Local Solutions , 2012 .

[22]  Patrick Pérez,et al.  ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  P. Stott Attribution: Weather risks in a warming world , 2015 .

[24]  Yoshua Bengio,et al.  On the Morality of Artificial Intelligence , 2019, ArXiv.

[25]  G. Giannachi Representing, Performing and Mitigating Climate Change in Contemporary Art Practice , 2012, Leonardo.

[26]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).