VehSat: a Large-Scale Dataset for Vehicle Detection in Satellite Images

Abstract

There has been this last decade a major improvement in earth observation imagery, both in terms of resolution and revisit rate. This technological leap comes with an increasing demand for detecting objects in satellite images. In this context, vehicle detection generates a great deal of interest in the scientific and industrial community. Indeed, detecting vehicles can have a range of applications from traffic monitoring to parking lots occupancy rate estimation. This interest is demonstrated in the wide range of public vehicle datasets that already exist. However, these datasets rely on the use of aerial images, acquired from planes, that are different in many aspects from satellite images. Machine learning algorithms trained on these datasets are therefore likely to underperform on satellite images. Therefore, we introduce a large-scale dataset for VEHicle detection on SATellite images (VehSat). To this end, we collected 4544 crops of satellite images, from 4 different satellites and on 8 different areas. In total, 36851 vehicles were annotated in various contexts and resolutions. To build a baseline for vehicle detection in satellite images, we also evaluate state-of-the-art object detection algorithms on VehSat. Results show that, although performing better than human annotators on crowdsourcing platforms, machine learning algorithms still have a lot of room for improvement due to the challenging nature of the dataset.