USE-SVI: A reproducible pipeline for sampling, acquiring, and stitching Street View imagery to support urban analytics

Street-level imagery (SLI) is increasingly used in urban analytics for tasks like estimating greenery, conducting transport audits, and assessing facades. However, inconsistent image quality, uneven spatial coverage, and non-standardized acquisition methods limit reproducibility. We introduce USE-SVI (Urban Sampling & Extraction of Street View Imagery), a reproducible process to sample, acquire, and stitch street-view images for city-wide analysis. The protocol ensures regular spatial coverage sampling points at fixed intervals, generates four viewing directions per point to capture main views, acquires images through official Street View APIs or open-licence platforms (e.g., Mapillary or KartaView) with detailed metadata recording, and creates panoramas using OpenCV (e.g., ORB keypoints, FLANN matching, Stitcher). This approach produces evenly spaced images, clear provenance, and ready-to-use outputs (CSV, PNG, XLSX), supporting machine learning and visual checks. By standardizing key steps, sampling, acquisition, and stitching, USE-SVI enhances transparency and scalability, adheres to platform terms, and enables replication across cities and periods. Limitations involve variable provider coverage and occasional stitching failures in scenes with few features.