Abstract
We present a large-scale, longitudinal visual dataset of urban streetlights captured by 22 fixed-angle cameras deployed across Bristol, U.K., from 2021 to 2025. The dataset contains over 526,000 images, collected hourly under diverse lighting, weather, and seasonal conditions. Each image is accompanied by rich metadata, including timestamps, GPS coordinates, and device identifiers. This unique real-world dataset enables detailed investigation of visual drift, anomaly detection, and MLOps strategies in smart city deployments. To promtoe seconardary analysis, we additionally provide a self-supervised framework based on convolutional variational autoencoders (CNN-VAEs). Models are trained separately for each camera node and for day/night image sets. We define two per-sample drift metrics: relative centroid drift, capturing latent space deviation from a baseline quarter, and relative reconstruction error, measuring normalized image-domain degradation. This dataset provides a realistic, fine-grained benchmark for evaluating long-term model stability, drift-aware learning, and deployment-ready vision systems. The images and structured metadata are publicly released in JPEG and CSV formats, supporting reproducibility and downstream applications such as streetlight monitoring, weather inference, and urban scene understanding. The dataset can be found at this https URL and this https URL.
Abstract (translated)
我们提供了一个大规模的、长期性的视觉数据集，该数据集涵盖了英国布里斯托尔市2021年至2025年间由22个固定角度摄像头捕捉的城市街道照明情况。此数据集中包含超过526,000张图像，并且这些图像是在各种光照条件、天气状况和季节变化下每小时收集的。每个图像都附有详细的元数据，包括时间戳、GPS坐标以及设备标识符等信息。 这一独特的现实世界数据集使得针对视觉漂移（visual drift）、异常检测（anomaly detection）以及智能城市部署中的MLOps策略进行详细研究成为可能。为了促进二次分析，我们还提供了一个基于卷积变分自动编码器（CNN-VAEs）的自我监督框架，并且模型分别在每个摄像头节点和白天/夜晚图像集合中独立训练。 我们定义了两种样本级别的漂移指标：相对中心点漂移（relative centroid drift），它捕捉到基准季度中隐含空间偏移；以及相对重建误差（relative reconstruction error），用于测量归一化后的图像领域退化程度。该数据集为长期模型稳定性、针对漂移的适应性学习及现成视觉系统的部署提供了一个现实且细致的标准。 这些图像和结构化的元数据将以JPEG和CSV格式公开发布，以支持重现性和下游应用（如街道照明监控、天气推断以及城市场景理解）。此数据集可在以下网址获取：[具体URL] 和 [具体URL]。请注意，在实际使用中需要将上述示例中的"[具体URL]"替换为实际的链接地址。
URL
https://arxiv.org/abs/2512.12205
