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International Journal of Computer Vision

19-03-2024

UrbanEvolver: Function-Aware Urban Layout Regeneration

Authors: Yiming Qin, Nanxuan Zhao, Jiale Yang, Siyuan Pan, Bin Sheng, Rynson W. H. Lau

Published in: International Journal of Computer Vision

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Abstract

Urban regeneration is an important strategy for land redevelopment, to address the urban decay in cities. Among many tasks, urban layout is the foundation for urban regeneration. In this paper, we target a new task called function-aware urban layout regeneration, and propose UrbanEvolver, a function-aware deep generative model for the task. Given a target region to be regenerated, our model outputs a regenerated urban layout (i.e., roads and buildings) for the target region by considering the function (i.e., land use type) of the target region and its surrounding context (i.e., the functions and urban layouts of the surrounding regions). UrbanEvolver first extracts implicit regeneration rules from the target function and the surrounding context by encoding them separately in different scales through the function-layout adaptive (FA) blocks, and then constrains the regenerated urban layout based on the learned regeneration rules. To enforce the regenerated layout to be valid and to follow the road structure, we design a set of losses covering both pixel-level and geometry-level constraints. To train our model, we collect a large-scale urban layout dataset covering more than 147 K regions under 1300 km\(^2\) with rich annotations, including functions, region shapes, urban road layouts, and urban building layouts. We conduct extensive experiments to show that our model outperforms the baseline methods in generating practical and function-aware urban layouts based on the given target function and surrounding context.

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Footnotes
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Metadata
Title
UrbanEvolver: Function-Aware Urban Layout Regeneration
Authors
Yiming Qin
Nanxuan Zhao
Jiale Yang
Siyuan Pan
Bin Sheng
Rynson W. H. Lau
Publication date
19-03-2024
Publisher
Springer US
Published in
International Journal of Computer Vision
Print ISSN: 0920-5691
Electronic ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-024-02030-w

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