Anomaly Detection on Mars Using Deep Learning

Anomaly Detection on Mars Using Deep Learning

  • The Results
Project finished!

38 Collaborators built an anomaly detection model for identifying past or present extraterrestrial technology on the surface of Mars. A U-Net model yielded the best scores with precision measures for all anomalies of above 90 percent.


Why applying anomaly detection on Mars

The second-smallest planet in the Solar System comprises a thin atmosphere and has surface features reminiscent both of the impact craters of the Moon and the valleys, deserts, and polar ice caps of Earth.

Recently looking for extraterrestrials in the form of technosignatures has gained new interest. These signatures are measurable properties that provide scientific evidence of past or present extraterrestrial technology. NASA wants scientists to evaluate how far the search for technosignatures has come and what the most promising possibilities for the future are.


The solutions

Below is a brief video demonstrating how the community worked together including the end results.

Among the many tasks accomplished, the team applied GAN`s for building an expert system that classifies images with an anomaly score. A Python package to process data from Mars efficiently. And lastly, testing various models to identify the best-fit model, which turned out to be U-Net model.

We are thanking all community collaborators for the amazing work done!

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Articles from the project