An urban infrastructure assessment system built on geo-tagged images and machine learning

Abstract

In modern era, the maintenance of public infrastructure often takes up a large share of financial budget for a city. The management of these urban assets is supported by a frequently updated inventory reflecting facility conditions. Traditional methods relying on inspection staff or sensors are faced with two main challenges: comprehensive and standardized data collection; quick and automatic assessment process. In this technical note, we introduce a unified method for condition assessment, purely based on street views and machine learning to develop perception quantification models with pairwise labeling datasets. In this way, the two problems could be solved with automatic and scalable processes, updatable algorithms, and affordable costs The method has been tested in the city of Ulaanbaatar, in which a benchmark covering the assessment of eight types of urban infrastructure (roadway, road curbs, road markings, road signs, sidewalks, catch basins, guardrails, and manholes) is demonstrated.