@inproceedings{zhu-etal-2025-plangpt-vl,
    title = "{P}lan{GPT}-{VL}: Enhancing Urban Planning with Domain-Specific Vision-Language Models",
    author = "Zhu, He  and
      Su, Junyou  and
      Chen, Minxin  and
      Wang, Wen  and
      Deng, Yijie  and
      Chen, Guanhua  and
      Zhang, Wenjia",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.169/",
    pages = "2461--2483",
    ISBN = "979-8-89176-333-3",
    abstract = "In the field of urban planning, existing Vision-Language Models (VLMs) frequently fail to effectively analyze planning maps, which are critical for urban planners and educational contexts. Planning maps require specialized understanding of spatial configurations, regulatory requirements, and multi-scale analysis.To address this challenge, we introduce PlanGPT-VL, the first domain-specific VLM tailored for urban planning maps. PlanGPT-VL employs three innovations:(1) PlanAnno-V framework for high-quality VQA data synthesis,(2) Critical Point Thinking (CPT) to reduce hallucinations through structured verification, and(3) PlanBench-V benchmark for systematic evaluation.Evaluation on PlanBench-V shows that PlanGPT-VL outperforms general-purpose VLMs on planning map interpretation tasks, with our 7B model achieving performance comparable to larger 72B models."
}