Traditional urban planning demands urban experts to spend considerable time
and effort producing an optimal urban plan under many architectural
constraints. The remarkable imaginative ability of deep generative learning
provides hope for renovating urban planning. While automated urban planners
have been examined, they are constrained because of the following: 1)
neglecting human requirements in urban planning; 2) omitting spatial
hierarchies in urban planning, and 3) lacking numerous urban plan data samples.
To overcome these limitations, we propose a novel, deep, human-instructed urban
planner. In the preliminary work, we formulate it into an encoder-decoder
paradigm. The encoder is to learn the information distribution of surrounding
contexts, human instructions, and land-use configuration. The decoder is to
reconstruct the land-use configuration and the associated urban functional
zones. The reconstruction procedure will capture the spatial hierarchies
between functional zones and spatial grids. Meanwhile, we introduce a
variational Gaussian mechanism to mitigate the data sparsity issue. Even though
early work has led to good results, the performance of generation is still
unstable because the way spatial hierarchies are captured may lead to unclear
optimization directions. In this journal version, we propose a cascading deep
generative framework based on generative adversarial networks (GANs) to solve
this problem, inspired by the workflow of urban experts. In particular, the
purpose of the first GAN is to build urban functional zones based on
information from human instructions and surrounding contexts. The second GAN
will produce the land-use configuration based on the functional zones that have
been constructed. Additionally, we provide a conditioning augmentation module
to augment data samples. Finally, we conduct extensive experiments to validate
the efficacy of our work.

采用深度生成学习构建基于人类指导、考虑空间层级的城市规划器，并引入生成对抗网络以解决空间层级问题。

自动化城市规划感知空间层次和人类指令

Automated Urban Planning aware Spatial Hierarchies and Human Instructions

In recent years, convolutional neural networks (CNN) have played an important
role in the field of deep learning. Variants of CNN's have proven to be very
successful in classification tasks across different domains. However, there are
two big drawbacks to CNN's: their failure to take into account of important
spatial hierarchies between features, and their lack of rotational invariance.
As long as certain key features of an object are present in the test data,
CNN's classify the test data as the object, disregarding features' relative
spatial orientation to each other. This causes false positives. The lack of
rotational invariance in CNN's would cause the network to incorrectly assign
the object another label, causing false negatives. To address this concern,
Hinton et al. propose a novel type of neural network using the concept of
capsules in a recent paper. With the use of dynamic routing and reconstruction
regularization, the capsule network model would be both rotation invariant and
spatially aware. The capsule network has shown its potential by achieving a
state-of-the-art result of 0.25% test error on MNIST without data augmentation
such as rotation and scaling, better than the previous baseline of 0.39%. To
further test out the application of capsule networks on data with higher
dimensionality, we attempt to find the best set of configurations that yield
the optimal test error on CIFAR10 dataset.

本文介绍了卷积神经网络的两个不足之处：不能考虑特征之间的重要空间关系和缺乏旋转不变性。为了解决这个问题，Hinton 等人提出了一种新型神经网络 —— 胶囊网络，该网络采用动态路由和重构正则化技术，具有旋转不变性和空间感知能力。在 MNIST 数据集上进行测试，胶囊网络的测试错误率为 0.25％，优于以前 0.39％的基准。同时，我们还尝试找到最佳配置组合，以在 CIFAR10 数据集上获得最佳测试结果。