UrbanEvolver

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 147K regions under 1300 km2 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.