首个面向高密度城中村的建筑道路数据集，助力精准城市治理
Building and Road Recognition in Dense Urban Informal Settlements: A Dataset and Benchmark
- 构建覆盖126个城中村的高分辨率遥感数据集
- 现有模型在密集建筑与窄路识别上准确率不足60%
- 适合城市规划、遥感分析与深度学习研究者使用
作为广泛存在的非正规住区形式，城中村对可持续城市发展与治理构成重大挑战。精确绘制其基础设施至关重要，但现有遥感数据集主要聚焦正规城市环境，缺乏针对城中村典型高密度建筑布局和狭窄道路网络的细粒度标注数据。为此，我们提出首个专为极密级非正规城市住区建筑与道路提取设计的高分辨率遥感数据集——DenseUIS，涵盖中国深圳与广州的126个城中村。同时，我们在该数据集上对当前主流深度学习模型进行了全面评估。实验结果表明，现有方法在处理城中村独特的形态特征时存在明显局限性，凸显了开发专用算法的必要性。DenseUIS因此为复杂高密度非正规环境中的细粒度城市测绘提供了可靠基准。数据集已公开于 https://github.com/rui-research/DenseUIS。
原文摘要 · Abstract (English)
As a widespread form of informal settlements, urban villages present significant challenges for sustainable urban development and governance. Precise mapping of their infrastructure is essential, however, existing remote sensing datasets primarily focus on formal urban environments, lacking fine-grained annotated data for the high-density building patterns and narrow road networks typical of urban villages. To address this gap, we introduce the \textit{DenseUIS} dataset, the first high-resolution remote sensing dataset specifically designed for building and road extraction in extremely dense urban informal settlements, covering 126 urban villages across Shenzhen and Guangzhou in China. Furthermore, we conduct a comprehensive evaluation of state-of-the-art deep learning models on this dataset. Experimental results reveal the limitations of existing methods in handling the unique morphological patterns of dense informal settlements, underscoring the need for specialized approaches. \textit{DenseUIS} therefore provides a robust benchmark for advancing fine-grained urban mapping in complex and high-density informal environments. The dataset is publicly available at https://github.com/rui-research/DenseUIS.
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