Automated Footprint Generation from Geotags with Kernel Density Estimation and Support Vector Machines

Abstract

A well-known problematic issue for the gazetteer services that many geospatial applications depend on is the lack of spatial footprints of imprecise regions. We present an automated method of footprint generation based on the statistical evaluation of a set of points, which are assumed to lie in the region. Two statistical methods, Kernel Density Estimation and Support Vector Machines (SVMs), are applied and compared for this task. The overall approach is evaluated using precise regions, and the results obtained from the two classes of methods are evaluated by means of statistical classification measures showing a slight superiority of SVMs. Finally, a priori choices for the input parameters of the methods are derived from the results and footprints of imprecise regions are generated in a completely automated process. The input dataset is acquired from georeferenced photographs freely available on the web.