DualControlNet：用文本提示生成土地利用地图与卫星图像
Generative AI for urban planning: synthesizing land-use maps with satellite imagery via DualControlNet
城市规划也能用生成式模型了，输入一句话就能画出高密度开发方案的土地利用图，还带卫星图对照，做城市场景实验的可以看看。
论文提出 DualControlNet，一个生成式土地利用模型，可同时合成高分辨率土地利用地图和卫星图像。模型在 Orlando 和 Chicago 超过 40,000 对图块上训练，采用土地用途、空间一致性和图像质量三类指标评估。相比单模态基线，土地用途多样性一致性提升 62.8%，建筑覆盖率一致性提升 16.3%，跨模态空间一致性翻倍以上。实验还显示它能响应诸如 "high-density development" 的文本提示，用于城市规划情景探索。
Generative AI for urban planning: synthesizing land-use maps with satellite imagery via DualControlNet
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.
