Dona Kuruppuaratchi
NASA Goddard/UMD, College Park
Organizers
Ramanakumar Sankar
University of California, Berkeley
Indhu Varatharajan
Stony Brook University
Andy Smith
University College London
Building connections to advance applications
Machine learning (ML), and other large-data analysis techniques, have become essential tools for analyzing the ever-increasing sizes of scientific data returned from planetary missions. In addition, ML methods have demonstrated potential to assist in mission planning and operations. Planetary science and space physics have often been fields defined by sparse data concerns and extreme environments. These aspects provide a uniquely challenging, and rewarding field to apply data science methods.
This seminar series aims to bring together researchers in planetary science, space physics, data science, and other domain applications of data science. We welcome presentations from a broad array of fields including: Earth based and planetary science applications, educational efforts, and basic data science research.
Tuesday at 9 AM US Pacific
Planetary-Scale Similarity Search for Mars Orbital Imagery with Foundation-Model Embeddings
Mars orbital archives now contain enough imagery that finding morphologically similar features is bottlenecked by search, not data. We present a planetary-scale similarity search system built on foundation-model embeddings over the full CTX Murray global mosaic (~26.9M indexed locations). A Vision Transformer pretrained via self-supervised learning on millions of CTX patches produces embeddings that capture surface texture and landform semantics without any labels. Deployed as a quantized vector index on a single server, the system supports sub-second instance-level retrieval ("find terrains like this"), geo-filtered search within regions of interest, and interactive relevance feedback for iterative refinement. The system is publicly accessible at findmars.space.
To receive invitations to our monthly series or to recommend speakers, please fill out our community form.
NASA Goddard/UMD, College Park
University of California, Berkeley
Stony Brook University
University College London
Space Sciences Lab, University of California Berkeley
Dublin Institute for Advanced Studies