  Layout planning is centrally important in the field of architecture and urban
design. Among the various basic units carrying urban functions, residential
community plays a vital part for supporting human life. Therefore, the layout
planning of residential community has always been of concern, and has attracted
particular attention since the advent of deep learning that facilitates the
automated layout generation and spatial pattern recognition. However, the
research circles generally suffer from the insufficiency of residential
community layout benchmark or high-quality datasets, which hampers the future
exploration of data-driven methods for residential community layout planning.
The lack of datasets is largely due to the difficulties of large-scale
real-world residential data acquisition and long-term expert screening. In
order to address the issues and advance a benchmark dataset for various
intelligent spatial design and analysis applications in the development of
smart city, we introduce Residential Community Layout Planning (ReCo) Dataset,
which is the first and largest open-source vector dataset related to real-world
community to date. ReCo Dataset is presented in multiple data formats with
37,646 residential community layout plans, covering 598,728 residential
buildings with height information. ReCo can be conveniently adapted for
residential community layout related urban design tasks, e.g., generative
layout design, morphological pattern recognition and spatial evaluation. To
validate the utility of ReCo in automated residential community layout
planning, two Generative Adversarial Network (GAN) based generative models are
further applied to the dataset. We expect ReCo Dataset to inspire more creative
and practical work in intelligent design and beyond. The ReCo Dataset is
published at: https://www.kaggle.com/fdudsde/reco-dataset.
