用可变边界动态定义城市区域，提升规划与政策的适应性。
Boundary Prompting: Elastic Urban Region Representation via Graph-based Spatial Tokenization
- 将城市实体建模为图结构中的节点，实现灵活区域划分。
- 支持在线提取区域特征，适应不同任务需求。
- 适合城市规划、资源分配等需要动态区域划分的场景。
城市区域表征对城市规划、资源配置和政策制定等应用至关重要。传统方法依赖固定预设的区域边界，难以捕捉真实城市区域的动态与复杂特性。本文提出边界提示城市区域表征框架（BPURF），一种支持弹性区域定义的新方法。BPURF包含两个核心组件：(1) 空间令牌词典，将城市实体作为令牌并整合至统一的令牌图中；(2) 区域令牌集表示模型，利用令牌聚合与多通道模型嵌入对应区域边界的令牌集。此外，我们提出快速令牌集提取策略，实现在训练与提示过程中的在线提取。该框架通过边界提示定义城市区域，支持多种边界形态并适配不同任务。大量实验验证了BPURF在捕捉城市区域复杂特征方面的有效性。
原文摘要 · Abstract (English)
Urban region representation is essential for various applications such as urban planning, resource allocation, and policy development. Traditional methods rely on fixed, predefined region boundaries, which fail to capture the dynamic and complex nature of real-world urban areas. In this paper, we propose the Boundary Prompting Urban Region Representation Framework (BPURF), a novel approach that allows for elastic urban region definitions. BPURF comprises two key components: (1) A spatial token dictionary, where urban entities are treated as tokens and integrated into a unified token graph, and (2) a region token set representation model which utilize token aggregation and a multi-channel model to embed token sets corresponding to region boundaries. Additionally, we propose fast token set extraction strategy to enable online token set extraction during training and prompting. This framework enables the definition of urban regions through boundary prompting, supporting varying region boundaries and adapting to different tasks. Extensive experiments demonstrate the effectiveness of BPURF in capturing the complex characteristics of urban regions.
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