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

翻译：深度学习等数据驱动方法正迅速推动行星科学的发展，尤其在火星探测领域。尽管近期取得了进展，但现有基准大多仍局限于封闭集监督视觉任务，无法支持面向地理空间发现的文本引导检索。我们提出了MarsRetrieval，这是一个用于评估火星地理空间发现视觉-语言模型的检索基准。MarsRetrieval包含三项任务：(1) 配对图文检索，(2) 地貌检索，以及 (3) 全球地理定位，涵盖多空间尺度及多样地貌成因。我们提出了一种以检索为核心的统一评估协议，用于基准测试多模态嵌入架构，包括对比式双塔编码器与生成式视觉-语言模型。评估结果表明MarsRetrieval具有挑战性：即使是强大的基础模型也往往难以捕捉领域特定的地貌区分特征。我们进一步证明，领域特定的微调对于行星场景中可泛化的地理空间发现至关重要。代码已发布于 https://github.com/ml-stat-Sustech/MarsRetrieval