A rule-based approach for generating urban footprint maps: from road network to urban footprint

Abstract

Decision and policymakers need urban footprint data for monitoring human impact on the urban ecosystem for politics and services. Deriving urban footprint is a challenging work since it has rapidly changing borders. The existing methods for deriving urban footprint map based on raster images have several steps such as determination of indicators and parameters of image classification. These steps limit the process by an operator since they require human decisions. This paper proposes a new rule-based approach for obtaining urban footprint based on Delaunay triangulation among selected centroids of roads and dead-end streets. The selection criterion is determined as maximum road length by using standard deviation operator. To produce urban footprints, this method needs no other data or information apart from road network geometry. This means that the proposed method uses only intrinsic indicators and measures. The experimental study was conducted with OpenStreetMap road data of Washington DC, Madrid, Stockholm, and Wellington. The comparisons with authority data prove that the proposed method is sufficient in many parts of urban and suburban lands.