Remote sensing change detection (CD) aims to identify where land-cover semantics change across time, but most existing methods still assume a fixed label space and therefore cannot answer arbitrary user-defined queries. Open-vocabulary change detection (OVCD) instead asks for the change mask of a queried concept. In the fully training-free setting, however, dense concept responses are difficult to compare directly across dates: appearance variation, weak cross-concept competition, and the spatial continuity of many land-cover categories often produce noisy, fragmented, and semantically unreliable change evidence. We propose Consistency-Regularized Open-Vocabulary Change Detection (CoRegOVCD), a training-free dense inference framework that reformulates concept-specific change as calibrated posterior discrepancy. Competitive Posterior Calibration (CPC) and the Semantic Posterior Delta (SPD) convert raw concept responses into competition-aware queried-concept posteriors and quantify their cross-temporal discrepancy, making semantic change evidence more comparable without explicit instance matching. Geometry-Token Consistency Gate (GeoGate) and Regional Consensus Discrepancy (RCD) further suppress unsupported responses and improve spatial coherence through geometry-aware structural verification and regional consensus. Across four benchmarks spanning building-oriented and multi-class settings, CoRegOVCD consistently improves over the strongest previous training-free baseline by 2.24 to 4.98 F1$_C$ points and reaches a six-class average of 47.50% F1$_C$ on SECOND.

翻译：遥感变化检测旨在识别跨时间的地表覆盖语义变化，但现有方法大多假设固定的标签空间，无法应答任意用户定义的查询。开放词汇变化检测则要求获取所查询概念的变化掩码。然而，在完全无训练设置下，密集概念响应难以跨日期直接比较：外观变化、弱跨概念竞争以及许多地表覆盖类别的空间连续性，常导致噪声、碎片化且语义不可靠的变化证据。我们提出一致性正则化开放词汇变化检测，这是一种无训练密集推理框架，将概念特定变化重新定义为校准后验差异。竞争性后验校准与语义后验差值将原始概念响应转换为具有竞争意识的目标概念后验，并量化其跨时间差异，使语义变化证据更易比较且无需显式实例匹配。几何-令牌一致门控与区域共识差异进一步通过几何感知结构验证与区域共识抑制不可信响应，提升空间连贯性。在涵盖面向建筑与多类设置的四个基准上，CoRegOVCD相较于先前最强无训练基线，F1$_C$持续提升2.24至4.98点，并在SECOND数据集上达到六类平均47.50%的F1$_C$。