Automatic Assessment of Open Street Maps Database Quality using Aerial Imagery

Open data initiatives such as OpenStreetMap (OSM) are a powerful crowd sourced approach to data collection. However due to their crowd-sourced nature the quality of the database heavily depends on the enthusiasm and determination of the public. We propose a novel method based on variational autoencoder generative adversarial networks (VAE-GAN) together with an information theoretic measure of database quality based on the expected discrimination information between the original image and labels generated from OSM data. Experiments on overhead aerial imagery and segmentation masks generated from OSM data show that our proposed discrimination information measure is a promising measure to regional database quality in OSM.