Learning Street View Representations with Spatiotemporal Contrast

TL;DR

This paper introduces a self-supervised contrastive learning framework that leverages spatiotemporal street view imagery to learn robust urban environment representations, improving performance in various city-related tasks.

Contribution

It proposes a novel spatiotemporal contrastive learning method that captures dynamic and built environment features from street view images, advancing urban visual representation learning.

Findings

Outperforms traditional supervised and unsupervised methods in visual place recognition.

Enhances socioeconomic estimation accuracy using learned representations.

Reveals different behaviors of representations across downstream tasks.

Abstract

Street view imagery is extensively utilized in representation learning for urban visual environments, supporting various sustainable development tasks such as environmental perception and socio-economic assessment. However, it is challenging for existing image representations to specifically encode the dynamic urban environment (such as pedestrians, vehicles, and vegetation), the built environment (including buildings, roads, and urban infrastructure), and the environmental ambiance (such as the cultural and socioeconomic atmosphere) depicted in street view imagery to address downstream tasks related to the city. In this work, we propose an innovative self-supervised learning framework that leverages temporal and spatial attributes of street view imagery to learn image representations of the dynamic urban environment for diverse downstream tasks. By employing street view images captured at the same location over time and spatially nearby views at the same time, we construct contrastive learning tasks designed to learn the temporal-invariant characteristics of the built environment and the spatial-invariant neighborhood ambiance. Our approach significantly outperforms traditional supervised and unsupervised methods in tasks such as visual place recognition, socioeconomic estimation, and human-environment perception. Moreover, we demonstrate the varying behaviors of image representations learned through different contrastive learning objectives across various downstream tasks. This study systematically discusses representation learning strategies for urban studies based on street view images, providing a benchmark that enhances the applicability of visual data in urban science. The code is available at https://github.com/yonglleee/UrbanSTCL.