Deepplanetary

Deepplanetary is an interdisciplinary field that applies deep learning techniques to planetary science. It encompasses analysis of data from solar system missions, terrestrial analog studies, and observations of exoplanets, with the aim of extracting physical properties, mapping surfaces and atmospheres, and accelerating discovery through automated interpretation of complex datasets.

Common methods include convolutional neural networks for high-resolution imagery, recurrent networks and transformers for time-series observations, and graph neural networks for modeling networks of geological features. Autoencoders and variational autoencoders are used for dimensionality reduction and data fusion, while generative models aid in simulating spectra or images under varying conditions.

Applications range from automated detection and classification of surface features (craters, rivers, dunes) to compositional mapping via spectroscopy, atmospheric retrievals for exoplanets, and inference of interior structure from gravity and topography data. Deepplanetary tools are employed to augment mission planning, error analysis, and rapid data triage.

Although not a formal field for all researchers, the term has gained use in academic and industry contexts since the 2010s as part of broader astroinformatics and AI-driven planetary science. Researchers typically rely on public archives from space agencies, along with telescope data, and share models and code through open repositories.

Challenges include limited labeled datasets across diverse planetary bodies, instrument-specific biases, and the need for uncertainty quantification and interpretability. Generalization across missions and planets remains difficult, motivating physics-informed approaches, active learning, and standardized benchmarks to evaluate cross-domain performance.

Future directions point to closer integration with physical models, multi-modal data fusion, and cross-mission training regimes to enable robust, scalable insights into planetary geology, atmospheres, and exoplanet characterization.