开源工具自动找街景图像对，提升城市感知研究精度。
PairWise Image Finder: An Open-source Tool for Finding Visually Aligned Street-Level Image Pairs for Urban Perception Studies
- 结合特征匹配与语义分割，量化图像视觉对齐度。
- 输出匹配率、距离和掩码对齐度，支持高质量筛选。
- 适合城市变化检测、感知研究等需要精准图像对的场景。
变化检测与场景识别技术广泛应用于街景影像（SVI）以理解多年间场景变迁。然而，仅靠元数据常难以可靠找到视觉对齐的图像对。本文提出 PairWise Image Finder 工具，融合特征检测与匹配，并利用语义分割掩码量化不同时期图像的视觉对齐程度。该工具输出匹配关键点占比、匹配特征距离与覆盖范围，以及语义掩码对齐情况，使用户可根据对齐质量与应用场景筛选图像对。通过该工具获取的视觉对齐图像对可用于精确研究纵向变化，显著降低感知研究中的人工标注成本。工具有效性通过纵向变化对比验证，凸显视角一致性在量化变化中的重要性。该方法为研究人员和利益相关方提供可扩展、开源的高质量图像对发现工具，适用于城市分析、感知研究等应用。
原文摘要 · Abstract (English)
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.
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