Detecting Moving Vehicles on Sentinel-2 Imagery Using Semi-Automatic Labeling From S2A/S2C Tandem Phase

Abstract. During the commissioning phase of ESA’s Sentinel-2C, tandem images with Sentinel-2A were acquired with a delay of 30 seconds. We present a novel, automated method for labeling moving vehicles in Sentinel-2 images, leveraging the temporal offset between these tandem acquisitions. We propose a filtering process that isolates pixels corresponding to vehicles that moved between the two acquisitions. We generate a training dataset based on this process, removing the need for a large manual labeling phase. The dataset is used to train a standard deep-learning-based vehicle detection model. Experimental results, as well as a validation study using ground-truth data from California, highlight the quality of the proposed labeling method, and show that a vehicle detection model can be successfully trained from quasi-simultaneous acquisitions.