A comparison of features Synthetic WAMI and GES of the same location

The rapid growth in deep learning has accelerated advances in many areas of computer vision. However, deep learning-based approaches require a large amount of data to train models. Subsequently, synthetic data is increasingly being looked to as a source for labeled training datasets to be used with supervised deep learning algorithms. WAMI (wide-area motion imagery) is sequential, oblique imagery, typically taken from an aircraft or drone, at city scale. Collecting a WAMI dataset can represent a significant investment of resources and logistics. However, the availability of synthetic WAMI datasets could overcome these concerns as well as potentially add benefits such as having associated ground truth. Recently, Google released Earth Studio, a browser-based animation tool that uses a 3D rendering engine to generate WAMI-like datasets across the globe. When working with synthetic data, a key point of concern is whether the synthetic data is sufficiently realistic for the purpose at hand. In this paper, we generate rendered WAMI datasets using Google Earth Studio. The rendered datasets are of the same locations for which we also have real WAMI datasets, and we then analyze the WAMI dataset and the images rendered by Google Earth Studio based on 3D reconstruction and feature evaluation of the dataset to determine how feasible synthetic datasets are in comparison to non-synthetic ones.