Terra Mars · 地球照耀火星

When Earth shines on Mars through AI’s imagination...

Ever since humankind was able to look back to Earth from space, the view of the "blue planet" seems to provoke a contemporary type of nostalgia and inspire poetic reflection. Images of Mars could arouse quite the opposite—the feeling of otherness, the realization of our loneliness in the universe. What I present here are artistic renderings of Mars with visual reference to our very own planet Earth. For this project, I have trained an artificial neural network with topographical data and satellite imagery of Earth so that it can learn the relation between them. The trained model was then applied to topographical data of Mars to generate images that resemble satellite imagery of Earth. Terra Mars suggests a new approach to creative applications of artificial intelligence—using its capability of remapping to broaden the domain of artistic imagination.

The generated global texture is extra high-resolution—consists of nearly a billion pixels, it enables gallery-quality rendering of the generated Mars as a globe, as a detailed map, and even as close-ups that the audience can dive into.

A space-station view when flying through some of the landmarks of the western hemisphere of Terra Mars generation 73.

If images of our planet arouse people's poetic emotions, would an alien planet that resembles our own arouse the same feelings or different ones? You are welcomed to interpret Terra Mars from various perspectives. You can enjoy it as a playful remix of the two planets, or you can relate this imaginary version to the astronomical facts of the "red planet." Maybe you consider it as a preview of a possible outcome of human's terraforming efforts, or you just appreciate the sheer beauty of a planet that resembles our own.

To me, the most intriguing aspect of this AI-based creative approach is the seemingly contradicting assessments we can make of the generated Mars imagery—based on the astronomical facts of Mars, the generated look of it is by no means scientific, but on the other hand, it is created by inferring solid logic learned from precise data by a meticulous and tireless artificial intelligence. The AI has learned every corner of the Earth terrain for hundreds of times and applied its knowledge to every decision during its generation of the Martian terrain. This is not an approach that can be carried out by a human artist alone.

Therefore, I would like to claim that AI is the new imagination. Its unique capabilities such as remapping between domains should inspire and support artists to create artworks the world has never seen.


Notes

  • Generation: A Terra Mars generation refers to the results of applying the trained model to the full dataset of Mars topography, and stitching the output into a global color map of Mars. The process usually but not always includes training a new model. The generations are numbered in chronological order.

Exhibitions

Publications

Data credits

  • "Blue Marble: Next Generation," (NASA, 2005).

  • "Blue Marble," (NASA, 2002).

  • "Mars HRSC MOLA Blended DEM Global 200m v2," (U.S. Geological Survey, 2017).

Neural network model credits

Early iterations of the Terra Mars series were based on the following neural network models:

  • P. Isola, et al. "Image-To-Image Translation With Conditional Adversarial Networks," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2017, 5967–5976.

  • T. C. Wang, et al. "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, 8798–8807.

Asset credits

  • Clouds texture: "Blue Marble," (NASA, 2002).

  • Stars texture: "Deep Star Maps," (NASA, 2012).


Terra Mars Series

Martian Earth belongs to the Terra Mars series. Projects in the series includes:

Terra Mars

2019

Martian Earth

2019