# PairWise Image Finder: An Open-source Tool for Finding Visually Aligned Street-Level Image Pairs for Urban Perception Studies

arXiv: https://arxiv.org/abs/2606.08795 · HTML: https://mlsanity.com/paper/2606.08795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 7 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [OneFormer: One Transformer to Rule Universal Image Segmentation](https://mlsanity.com/paper/2211.06220/) (2022)
- [ZeroSCD: Zero-Shot Street Scene Change Detection](https://mlsanity.com/paper/2409.15255/) (2024)
- [CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series](https://mlsanity.com/paper/2401.01107/) (2024)
- [Detecting Neighborhood Gentrification at Scale via Street-level Visual Data](https://mlsanity.com/paper/2301.01842/) (2023)
- [Environmental Change Detection for Real-World Change Analysis](https://mlsanity.com/paper/2506.11481/) (2025)

## Built on by (most cited first, 1 of 1)

- [You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change](https://mlsanity.com/paper/2609.00649/) (2026)

## Abstract (arXiv)

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.