MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars

Data-driven approaches like deep learning are rapidly advancing planetary science, particularly in Mars exploration. Despite recent progress, most existing benchmarks remain confined to closed-set supervised visual tasks and do not support text-guided retrieval for geospatial discovery.

We introduce MarsRetrieval, a retrieval benchmark for evaluating vision-language models for Martian geospatial discovery. MarsRetrieval includes three tasks: (1) paired image-text retrieval, (2) landform retrieval, and (3) global geo-localization, covering multiple spatial scales and diverse geomorphic origins.

We propose a unified retrieval-centric protocol to benchmark multimodal embedding architectures, including contrastive dual-tower encoders and generative vision-language models. Our evaluation shows MarsRetrieval is challenging: even strong foundation models often fail to capture domain-specific geomorphic distinctions.

We further show that domain-specific fine-tuning is critical for generalizable geospatial discovery in planetary settings. Our code is available at https://github.com/ml-stat-Sustech/MarsRetrieval

Shuoyuan Wang, Yiran Wang, Hongxin Wei

Subjects: Computer Vision and Pattern Recognition (cs.CV); Instrumentation and Methods for Astrophysics (astro-ph.IM); Computation and Language (cs.CL)
Cite as: arXiv:2602.13961 [cs.CV] (or arXiv:2602.13961v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2602.13961
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Submission history
From: Shuoyuan Wang
[v1] Sun, 15 Feb 2026 02:41:56 UTC (5,148 KB)
https://arxiv.org/abs/2602.13961
Astrobiology, Astrogeology,