  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.
