AI Astrobiology Exploration Science

NEW LITERATURE ADDED REGULARLY

2024:

Leveraging Machine Learning Approaches to Predict Organic Carbon Abundance in Mars-Analog Hypersaline Lake Sediments. 2024, Nichols, N. et al. JGR Machine Learning and Computation. 

https://doi.org/10.1029/2024JH000138

Keywords: Machine Learning, Mars, In-situ, X-ray fluorescence

2023:

Towards a Biosignatures Image Detection System for Planetary Exploration with UAVs. 2023, Galvez-Serna, J. et al. IEEE Aerospace Conference.

https://ieeexplore.ieee.org/document/10115661

Keywords: Biosignatures, CNN, Imaging

Orbit-to-ground framework to decode and predict biosignature patterns in terrestrial analogues. 2023, Warren-Rhodes, K. Nat Astron.

https://doi.org/10.1038/s41550-022-01882-x

Keywords: Biosignatures, CNN, Deep Learning, Mapping

2022:

Planetary Mapping Using Deep Learning: A Method to Evaluate Feature Identification Confidence Applied to Habitats in Mars-Analog Terrain. 2022, Phillips M. S. et al., Astrobiology Journal.

http://doi.org/10.1089/ast.2022.0014

Keywords: Biosignatures, CNN, Deep Learning, Mapping

Science Autonomy and Space Science: Application to the ExoMars Mission. 2022, da Poian, V. et al. Front. Astron. Space Sci.

https://doi.org/10.3389/fspas.2022.848669

Keywords: Training data, Logistic Regression, Random Forest

Autonomy for Ocean Worlds Astrobiology: A Perspective. 2022, Theiling B. P. et al. Astrobiology Journal.

http://doi.org/10.1089/ast.2021.0062

Keywords: Review, Neural Net, Feature Detection, Training