Building generalization is an essential task in generating multi-scale topographic maps. The progress of deep learning offers a new paradigm to overcome the coordination challenges faced by conventional building generalization algorithms. Some studies have confirmed the feasibility of several original semantic segmentation networks, such as U-Net and its variants and the conditional generative adversarial network (cGAN), for building generalization in image maps. However, they suffer from critical deformation effects, especially for large and geometrically complex buildings. Since learning building generalization essentially means modeling the subtle transformation of building footprints across scales, we argue that the spatial awareness of a neural network, for instance, regarding building size and shape, is crucial to effective learning. Thus, we propose a spatially-aware generative adversarial network, SpaGAN. It takes a representative cGAN, pix2pix, as the backbone, and modifies two modules: In the U-Net-based generator, an atrous spatial pyramid pooling (ASPP) module replaces the conventional convolutional module to extract multi-scale features of buildings of varying sizes and shapes; in the PatchGAN-based discriminator, a signed distance map (SDM) module is used to capture the fine-grained shape difference for discrimination. The proposed network was comprehensively evaluated with a synthetic and a real-world dataset. The results demonstrate that SpaGAN outperforms existing baseline models (U-Net, ResU-Net, pix2pix) for building generalization, particularly in the real-world dataset. The new model can achieve more reasonable aggregation, simplification, and squaring generalization operators.

中文翻译：

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SpaGAN：用于在图像映射中构建泛化的空间感知生成对抗网络


构建制图综合是生成多比例地形图的一项基本任务。深度学习的进步为克服传统建筑泛化算法面临的协调挑战提供了一种新的范式。一些研究已经证实了几种原始语义分割网络的可行性，例如 U-Net 及其变体和条件生成对抗网络 （cGAN），用于在图像地图中构建泛化。然而，它们会受到临界变形效应的影响，特别是对于大型和几何复杂的建筑物。由于学习建筑泛化本质上意味着对建筑物足迹跨尺度的细微转换进行建模，因此我们认为神经网络的空间感知，例如，关于建筑物的大小和形状，对于有效学习至关重要。因此，我们提出了一个空间感知的生成对抗网络 SpaGAN。它以具有代表性的 cGAN pix 为骨干，并修改了两个模块：在基于 U-Net 的生成器中，一个空洞空间金字塔池化 （ASPP） 模块取代了传统的卷积模块，以提取不同大小和形状的建筑物的多尺度特征;在基于 PatchGAN 的判别器中，使用有符号距离图 （SDM） 模块来捕获细粒度的形状差异以进行区分。使用合成数据集和真实数据集对所提出的网络进行了全面评估。结果表明，SpaGAN 在构建泛化方面优于现有的基线模型（U-Net、ResU-Net、pix2pix），尤其是在真实数据集中。新模型可以实现更合理的聚合、简化和平方泛化算子。