场景感知的城市设计：一种使用共现嵌入与视觉语言模型的人机协同推荐框架
Scene-Aware Urban Design: A Human-AI Recommendation Framework Using Co-Occurrence Embeddings and Vision-Language Models
AI总结：
本文提出一种人机协同的城市设计推荐框架，利用共现嵌入和视觉语言模型，从场景图像中为锚定物体推荐互补元素，以支持自下而上的本地化参与式设计。
摘要：
本文介绍了一个人在环路的计算机视觉框架，该框架利用生成式人工智能在公共空间中提出微观尺度的设计干预，并支持更具连续性的本地化参与。系统使用 Grounding DINO 和 ADE20K 数据集中经过筛选的子集作为城市建成环境的代理，检测城市物体并构建共现嵌入，以揭示常见的空间配置。基于该分析，用户会收到与所选锚定物体在统计上最可能互补的五个候选对象。随后，视觉语言模型对场景图像和所选配对进行推理，建议第三个物体以构成更复杂的城市策略。该工作流程将选择与优化权保留在人手中，旨在超越自上而下的总体规划，将设计选择扎根于日常模式和生活经验之中。
英文摘要：
This paper introduces a human-in-the-loop computer vision framework that uses generative AI to propose micro-scale design interventions in public space and support more continuous, local participation. Using Grounding DINO and a curated subset of the ADE20K dataset as a proxy for the urban built environment, the system detects urban objects and builds co-occurrence embeddings that reveal common spatial configurations. From this analysis, the user receives five statistically likely complements to a chosen anchor object. A vision language model then reasons over the scene image and the selected pair to suggest a third object that completes a more complex urban tactic. The workflow keeps people in control of selection and refinement and aims to move beyond top-down master planning by grounding choices in everyday patterns and lived experience.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
