Generating DTM From DSM Using a Conditional GAN in Built-Up Areas

Abstract

The recent surge in floods requires the development of a digital terrain model (DTM) with a high spatial resolution, as this type of model is essential for risk assessment at the building level. To generate a DTM, it is necessary to remove nonground objects from the corresponding digital surface model (DSM) and interpolate the elevation of the removed area. However, automatically conducting this process requires input data other than the DSM and/or setting parameters that are suitable for the target region. Here, to directly generate a DTM from only a DSM, we used the pix2pix model that performs domain conversion using conditional generative adversarial networks (cGANs). Pix2pix and the generator-only model were tested in areas with different topography and building characteristics. For both the models, the root mean square errors (RMSEs) of the generated DTMs were approximately 0.4 m for areas where small buildings were distributed on flat terrain. In areas with large buildings with flat roofs or rapid elevation changes, the RMSEs were 1 m or even larger. In areas with rapid elevation changes, pix2pix showed an RMSE that was 1 m smaller than that of the generator-only model, suggesting the effectiveness of cGANs.