变化检测与场景识别技术已被广泛应用于街景影像（SVI），以理解跨年度场景的变化。然而，仅依赖元数据往往不足以可靠地找到视觉上对齐的图像对。本研究提出 PairWise Image Finder 工具，该工具融合特征检测与匹配，并借助语义分割掩膜来量化不同时期两幅图像之间的视觉对齐程度。该工具输出匹配关键特征的比例、匹配特征的距离与覆盖范围，以及语义掩膜的对齐度，使用户可根据对齐质量与具体应用场景筛选图像对。由此获得的视觉对齐图像对可用于精确开展显式纵向变化分析，并有助于降低城市感知研究中的人工工作量。本研究通过纵向变化对比分析验证了该工具的可用性，并强调了在量化变化时视角的重要性。所提出的方案为研究人员与相关利益方提供了一种可扩展、开源的工具，用于城市分析、感知及相关应用中高质量图像对的查找。

Change detection and scene recognition techniques have been widely applied to Street View Imagery (SVI) to understand changes in scenes across the years. However, metadata alone is often insufficient to reliably find visually aligned image pairs. This study introduces the PairWise image finder, a tool that integrates feature detection and matching, supported by semantic segmentation masks to quantify the visual alignment of two images of varying time periods. The tool outputs the share of matched key features, the matched feature distance and coverage, and the alignment of semantic masks, which enables the user to filter image pairs depending on the alignment quality and use case. The visually aligned pairs derived from the tool can be used to accurately study explicit longitudinal change and help reduce manual effort for perception studies. The usability of the tool is demonstrated through a comparison of longitudinal changes, highlighting the importance of perspective when quantifying changes. The proposed method provides a scalable and open tool for researchers and stakeholders to find high-quality image pairs for urban analysis, perception and related applications.