  Urban planning refers to the efforts of designing land-use configurations.
Effective urban planning can help to mitigate the operational and social
vulnerability of a urban system, such as high tax, crimes, traffic congestion
and accidents, pollution, depression, and anxiety. Due to the high complexity
of urban systems, such tasks are mostly completed by professional planners.
But, human planners take longer time. The recent advance of deep learning
motivates us to ask: can machines learn at a human capability to automatically
and quickly calculate land-use configuration, so human planners can finally
adjust machine-generated plans for specific needs? To this end, we formulate
the automated urban planning problem into a task of learning to configure
land-uses, given the surrounding spatial contexts. To set up the task, we
define a land-use configuration as a longitude-latitude-channel tensor, where
each channel is a category of POIs and the value of an entry is the number of
POIs. The objective is then to propose an adversarial learning framework that
can automatically generate such tensor for an unplanned area. In particular, we
first characterize the contexts of surrounding areas of an unplanned area by
learning representations from spatial graphs using geographic and human
mobility data. Second, we combine each unplanned area and its surrounding
context representation as a tuple, and categorize all the tuples into positive
(well-planned areas) and negative samples (poorly-planned areas). Third, we
develop an adversarial land-use configuration approach, where the surrounding
context representation is fed into a generator to generate a land-use
configuration, and a discriminator learns to distinguish among positive and
negative samples.
