Andrew Kirmse

Automatic alignment of large-scale aerial rasters to road-maps

This paper introduces a practical approach to register large-scale GIS imagery to a database of road vectors automatically. The proposed approach breaks the global alignment problem into a set of localized domains (tiles). Within each tile, the displacement between imagery and vectors is approximated by a translation. Finally, a global thin-plate-spline warp based on these local approximations is applied to register the imagery to the vector data. The critical step in this approach is a fully automatic algorithm to compute the best imagery-to-vectors translation within a tile. The proposed algorithm performs vector-guided extraction of road features, aggregates features obtained in the neighborhood of multiple vectors, and then estimates the best translation through a least-squares optimization applied to a selected subset of the aggregated features. It also computes a confidence value for each processed image tile, so that a human operator can easily find out the places where the automatic approach has encountered difficulties, if necessary. The algorithm has been tested on hundreds of production satellite images of different countries. It has correctly registered over 80 percent of the imagery, and consistently reported low confidence values for the rest.

Extracting patterns from location history

In this paper, we describe how a users location history (recorded by tracking the users mobile device location with his permission) is used to extract the users location patterns. We describe how we compute the users commonly visited places (including home and work), and commute patterns. The analysis is displayed on the Google Latitude history dashboard [7] which is only accessible to the user.