City models and height maps of urban areas serve as a valuable data source for numerous applications, such as disaster management or city planning. While this information is not globally available, it can be substituted by digital surface models (DSMs), automatically produced from inexpensive satellite imagery. However, stereo DSMs often suffer from noise and blur. Furthermore, they are heavily distorted by vegetation, which is of lesser relevance for most applications. Such basic models can be filtered by convolutional neural networks (CNNs), trained on labels derived from digital elevation models (DEMs) and 3D city models, in order to obtain a refined DSM. We propose a modular multi-task learning concept that consolidates existing approaches into a generalized framework. Our encoder-decoder models with shared encoders and multiple task-specific decoders leverage roof type classification as a secondary task and multiple objectives including a conditional adversarial term. The contributing single-objective losses are automatically weighted in the final multi-task loss function based on learned uncertainty estimates. We evaluated the performance of specific instances of this family of network architectures. Our method consistently outperforms the state of the art on common data, both quantitatively and qualitatively, and generalizes well to a new dataset of an independent study area.

市区的城市模型和高度图可作为许多应用程序（例如灾难管理或城市规划）的宝贵数据源。尽管此信息不是全球可用的，但可以用由廉价卫星图像自动生成的数字表面模型（DSM）代替。但是，立体声DSM经常遭受噪声和模糊的困扰。此外，它们被植被严重扭曲，这对于大多数应用而言意义不大。这样的基本模型可以通过卷积神经网络（CNN）进行过滤，并在从数字高程模型（DEM）和3D城市模型派生的标签上进行训练，以获得精确的DSM。我们提出了一个模块化的多任务学习概念，它将现有的方法整合到一个通用的框架中。我们的编码器/解码器模型具有共享编码器和多个特定于任务的解码器，它们利用车顶类型分类作为次要任务和多个目标，包括条件对抗项。基于学习到的不确定性估计，最终最终的多任务损失函数会自动对贡献的单目标损失进行加权。我们评估了该网络体系结构家族的特定实例的性能。我们的方法在数量和质量上都始终优于现有数据，并且可以很好地推广到独立研究区域的新数据集。基于学习到的不确定性估计，最终最终的多任务损失函数会自动对贡献的单目标损失进行加权。我们评估了该网络体系结构家族的特定实例的性能。我们的方法在数量和质量上都始终优于现有数据，并且可以很好地推广到独立研究区域的新数据集。基于学习到的不确定性估计，最终最终的多任务损失函数会自动对贡献的单目标损失进行加权。我们评估了该网络体系结构家族的特定实例的性能。我们的方法在数量和质量上都始终优于现有数据，并且可以很好地推广到独立研究区域的新数据集。