Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning

TL;DR

This paper introduces a deep human-guided conditional variational autoencoder framework for automated urban planning, enabling personalized, hierarchical, and robust land-use configuration generation despite data sparsity.

Contribution

It proposes a novel deep learning model that incorporates human textual guidance and spatial hierarchy, addressing data sparsity and enhancing urban planning generation.

Findings

Improved land-use configuration quality and diversity.

Effective integration of human guidance via text inputs.

Enhanced robustness and generalization in urban planning models.

Abstract

Urban planning designs land-use configurations and can benefit building livable, sustainable, safe communities. Inspired by image generation, deep urban planning aims to leverage deep learning to generate land-use configurations. However, urban planning is a complex process. Existing studies usually ignore the need of personalized human guidance in planning, and spatial hierarchical structure in planning generation. Moreover, the lack of large-scale land-use configuration samples poses a data sparsity challenge. This paper studies a novel deep human guided urban planning method to jointly solve the above challenges. Specifically, we formulate the problem into a deep conditional variational autoencoder based framework. In this framework, we exploit the deep encoder-decoder design to generate land-use configurations. To capture the spatial hierarchy structure of land uses, we enforce the decoder to generate both the coarse-grained layer of functional zones, and the fine-grained layer of POI distributions. To integrate human guidance, we allow humans to describe what they need as texts and use these texts as a model condition input. To mitigate training data sparsity and improve model robustness, we introduce a variational Gaussian embedding mechanism. It not just allows us to better approximate the embedding space distribution of training data and sample a larger population to overcome sparsity, but also adds more probabilistic randomness into the urban planning generation to improve embedding diversity so as to improve robustness. Finally, we present extensive experiments to validate the enhanced performances of our method.