CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials

TL;DR

CitySurfaces is a computer vision framework that uses street-level images and active learning to accurately classify sidewalk materials at city scale, aiding sustainable urban planning.

Contribution

The paper introduces a novel active learning-based computer vision framework for large-scale sidewalk material classification using street-level images, applicable across diverse cities.

Findings

Achieved 90.5% mIoU score on NYC and Boston data

Demonstrated applicability across six different cities

Provides a low-cost, accurate method for sidewalk material mapping

Abstract

While designing sustainable and resilient urban built environment is increasingly promoted around the world, significant data gaps have made research on pressing sustainability issues challenging to carry out. Pavements are known to have strong economic and environmental impacts; however, most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data with lower implementation costs and higher accuracy. In this paper, we propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials using widely available street-level images. We trained the framework on images from New York City and Boston and the evaluation results show a 90.5% mIoU score. Furthermore, we evaluated the framework using images from six different cities, demonstrating that it can be applied to regions with distinct urban fabrics, even outside the domain of the training data. CitySurfaces can provide researchers and city agencies with a low-cost, accurate, and extensible method to collect sidewalk material data which plays a critical role in addressing major sustainability issues, including climate change and surface water management.