Towards a Deep Automatic Generation of Figure-ground Maps

Abstract

Figure-ground maps play a key role in many disciplines where urban planning or analysis is involved. In this context, the automatic generation of such maps with respect to certain requirements and constraints is an important task. This paper presents a first step towards a deep automatic generation of figure-ground maps where the built density of the generated scenes is controlled and taken into account. This is preformed building upon a Geographic Data Translation model which has been applied to generate less available geospatial features, e.g. building footprints, from more widely available geospatial data, e.g. street network data, using conditional Generative Adversarial Networks. A novel processing approach is introduced to incorporate the population density and the built density accordingly. Furthermore, the impact of both the level of detail of the street network, i.e. its sparsity or density, and the spatial resolution of the training data on the generated figure-ground maps has been investigated. The generated maps and the qualitative results reveal an obvious impact of these parameters on the layout of built and unbuilt areas. Our approach paves the way for the expansion of existing districts by figure-ground maps of future neighbourhoods considering factors such as density and further parameters which will be subject of future work.