---
title: "Generative AI for urban planning: synthesizing land-use maps with satellite imagery via DualControlNet"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05859v1"
permalink: "https://feed.astralor.com/items/itm_d57da5395c2197beeb831004"
published: "2026-11-04T02:49:55.000Z"
score: 59
channel: "tech"
tags: ["RAG","多模态","Generative","DualControlNet Various","However","Various","Meanwhile","DualControlNet"]
---

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

## 速览

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 m…

## 关键点

- 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.

## 全文

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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