Fingerprinting New York City's Scaffolding Problem with Longitudinal Dashcam Data

TL;DR

This study employs computer vision on extensive dashcam imagery to map, track, and identify unpermitted scaffolding in NYC, providing a novel approach to urban infrastructure monitoring and permit compliance.

Contribution

It introduces a longitudinal, large-scale visual analysis method to detect and match scaffolding with permits, highlighting unpermitted structures in real-world city environments.

Findings

Identified over 850,000 scaffolding images and 5,156 active sheds.

Estimated 529 unpermitted scaffolds.

Developed a city-wide scaffolding tracking algorithm.

Abstract

Scaffolds, also called sidewalk sheds, are intended to be temporary structures to protect pedestrians from construction and repair hazards. However, some sidewalk sheds are left up for years. Long-term scaffolding becomes eyesores, creates accessibility issues on sidewalks, and gives cover to illicit activity. Today, there are over 8,000 active permits for scaffolds in NYC; the more problematic scaffolds are likely expired or unpermitted. This research uses computer vision on street-level imagery to develop a longitudinal map of scaffolding throughout the city. Using a dataset of 29,156,833 dashcam images taken between August 2023 and January 2024, we develop an algorithm to track the presence of scaffolding over time. We also design and implement methods to match detected scaffolds to reported locations of active scaffolding permits, enabling the identification of sidewalk sheds without corresponding permits. We identify 850,766 images of scaffolding, tagging 5,156 active sidewalk sheds and estimating 529 unpermitted sheds. We discuss the implications of an in-the-wild scaffolding classifier for urban tech, innovations to governmental inspection processes, and out-of-distribution evaluations outside of New York City.