  The essential task of urban planning is to generate the optimal land-use
configuration of a target area. However, traditional urban planning is
time-consuming and labor-intensive. Deep generative learning gives us hope that
we can automate this planning process and come up with the ideal urban plans.
While remarkable achievements have been obtained, they have exhibited
limitations in lacking awareness of: 1) the hierarchical dependencies between
functional zones and spatial grids; 2) the peer dependencies among functional
zones; and 3) human regulations to ensure the usability of generated
configurations. To address these limitations, we develop a novel
human-instructed deep hierarchical generative model. We rethink the urban
planning generative task from a unique functionality perspective, where we
summarize planning requirements into different functionality projections for
better urban plan generation. To this end, we develop a three-stage generation
process from a target area to zones to grids. The first stage is to label the
grids of a target area with latent functionalities to discover functional
zones. The second stage is to perceive the planning requirements to form urban
functionality projections. We propose a novel module: functionalizer to project
the embedding of human instructions and geospatial contexts to the zone-level
plan to obtain such projections. Each projection includes the information of
land-use portfolios and the structural dependencies across spatial grids in
terms of a specific urban function. The third stage is to leverage
multi-attentions to model the zone-zone peer dependencies of the functionality
projections to generate grid-level land-use configurations. Finally, we present
extensive experiments to demonstrate the effectiveness of our framework.
