Images+Weather: Collection, Validation, and Refinement

The time and location that an image is captured indirectly defines the appearance of the scene. However, for outdoor scenes, the more directly relevant variables that affect image appearance are the scene structure, local weather conditions, and the position of the sun. In this paper, we present a large dataset of archived time-lapse imagery with associated geo-location and weather annotations collected from multiple sources. Through validation via crowdsourcing, we estimate the reliability of this automatically gathered data. We use this dataset to investigate the value of direct geo-temporal context for the problem of predicting the scene appearance and show that explicitly incorporating the sun position and weather variables significantly reduces reconstruction error.