Artifacts of Idiosyncracy in Global Street View Data

TL;DR

This paper investigates how unique city characteristics influence biases in street view datasets, revealing coverage gaps and proposing evaluation methods to understand and address these biases.

Contribution

It uncovers biases caused by city idiosyncrasies in street view data and introduces a method for evaluating coverage distribution to improve dataset representativeness.

Findings

Identified coverage biases linked to city layout and idiosyncrasies.

Proposed a quantitative evaluation method for coverage distribution.

Case study of Amsterdam highlights impact of collection biases.

Abstract

Street view data is increasingly being used in computer vision applications in recent years. Machine learning datasets are collected for these applications using simple sampling techniques. These datasets are assumed to be a systematic representation of cities, especially when densely sampled. Prior works however, show that there are clear gaps in coverage, with certain cities or regions being covered poorly or not at all. Here we demonstrate that a cities' idiosyncracies, such as city layout, may lead to biases in street view data for 28 cities across the globe, even when they are densely covered. We quantitatively uncover biases in the distribution of coverage of street view data and propose a method for evaluation of such distributions to get better insight in idiosyncracies in a cities' coverage. In addition, we perform a case study of Amsterdam with semi-structured interviews, showing how idiosyncracies of the collection process impact representation of cities and regions and allowing us to address biases at their source.