Unsupervised Non-parametric Geospatial Modeling from Ground Imagery

Densely and regularly sampled ground-level imagery collected from semi-automated vehicle collection platforms (e.g. Street View^TM or EarthMine^TM) is rapidly becoming available on a global scale. Ground level views of neighborhoods around the world can provide unique regional indicators (e.g. demographics, zoning, socioeconomic health) at a finer resolution than current census data. To this end, this paper introduces the first unsupervised method for automated hierarchical modeling of high-level latent regions using densely sampled, geotagged ground imagery at a global scale. Our unsupervised, nonparametric approach models a multi-scale regularly sampled grid of panoramic ground-level images (where available), producing a multi-scale, multivariate estimate of the latent types of locations (e.g. commercial street, wooded area, park, country road, suburban street, etc.). Region types are model distributions of location types characteristic of particular neighborhoods, towns, or cities. These latent region types are used to cluster all locations into distinct geographic entities with regional signatures that can be used for comparison. We show the effectiveness of this method for discovering regional distributions at vastly different scales, including the Boston area, the United States, and the World.