Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change captioning datasets typically describe overall image-level differences, leaving fine-grained localized semantic reasoning largely unexplored. To close this gap, we present RSRCC, a new benchmark for remote sensing change question-answering containing 126k questions, split into 87k training, 17.1k validation, and 22k test instances. Unlike prior datasets, RSRCC is built around localized, change-specific questions that require reasoning about a particular semantic change. To the best of our knowledge, this is the first remote sensing change question-answering benchmark designed explicitly for such fine-grained reasoning-based supervision. To construct RSRCC, we introduce a hierarchical semi-supervised curation pipeline that uses Best-of-N ranking as a critical final ambiguity-resolution stage. First, candidate change regions are extracted from semantic segmentation masks, then initially screened using an image-text embedding model, and finally validated through retrieval-augmented vision-language curation with Best-of-N ranking. This process enables scalable filtering of noisy and ambiguous candidates while preserving semantically meaningful changes. The dataset is available at https://huggingface.co/datasets/google/RSRCC.

翻译：传统变化检测仅识别变化发生的位置，但无法用自然语言解释变化内容。现有遥感变化描述数据集通常描述整体图像层面的差异，而细粒度的局部语义推理仍鲜有探索。为填补这一空白，我们提出RSRCC——一个面向遥感变化问答的新基准，包含12.6万个问题，划分为8.7万训练样本、1.71万验证样本和2.2万测试样本。与以往数据集不同，RSRCC围绕局部化、特定变化的问题构建，需要针对特定语义变化进行推理。据我们所知，这是首个明确针对这种细粒度推理监督设计的遥感变化问答基准。为构建RSRCC，我们提出一种分层半监督策展流程，将“最优N选”排序作为关键的最后歧义消解阶段。首先从语义分割掩膜中提取候选变化区域，随后利用图像-文本嵌入模型进行初步筛选，最终通过结合“最优N选”排序的检索增强视觉-语言策展进行验证。该流程能够可扩展地过滤噪声和歧义候选，同时保留具有语义意义的变化。数据集可于https://huggingface.co/datasets/google/RSRCC获取。