# Generative AI for urban planning: synthesizing land-use maps with satellite imagery via DualControlNet

**Authors:** Qingqi Song, Yuzhou Chen, Lingqian Hu, Shenhao Wang  
**Lab:** University of Florida · **Published:** 4 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.05859) · [PDF](https://arxiv.org/pdf/2611.05859) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.05859.html)

## Key numbers

- **62.8%**: DualControlNet improves land-use diversity consistency by 62.8% and building coverage ratio consistency by 16.3%
- **16.3%**: DualControlNet improves land-use diversity consistency by 62.8% and building coverage ratio consistency by 16.3%

## Abstract

Various land-use models characterize land-use patterns and facilitate scenario planning. However, existing models are largely predictive, forecasting transitions based on numeric data without synthesizing new land-use maps from simple text prompts. Meanwhile, generative AI in urban systems remains unimodal, weakly controllable, and evaluated mostly by generic image quality rather than domain-specific metrics. To address these gaps, we introduce DualControlNet, a generative land-use model that jointly synthesizes high-resolution land-use maps and satellite imagery, addressing multimodality, controllability, and verification challenges. Trained on over 40,000 paired tiles from Orlando and Chicago, DualControlNet is evaluated using land-use, spatial consistency, and image quality metrics. Relative to unimodal baselines, DualControlNet improves land-use diversity consistency by 62.8% and building coverage ratio consistency by 16.3%, more than doubles cross-modal spatial consistency, and triples visual quality. Scenario-based experiments demonstrate that DualControlNet supports sustainable development through multidimensional controls, increasing density and diversity, and responding to text prompts like "high-density development." Overall, this work advances land-use modeling via a multimodal, controllable, and verifiable generative framework, lowering technical barriers and enabling intuitive exploration of urban futures.

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