Building and Road Recognition in Dense Urban Informal Settlements: A Dataset and Benchmark

2026-05-28

AI Summary

This study addresses the critical lack of fine-grained annotations for high-density urban informal settlements—particularly dense clusters of buildings and narrow road networks—in existing remote sensing datasets, which hinders accurate infrastructure mapping. To bridge this gap, the authors introduce DenseUIS, the first high-resolution remote sensing dataset specifically designed for extremely dense urban informal areas, encompassing 126 such neighborhoods across Shenzhen and Guangzhou, with meticulously hand-labeled building and road masks. Leveraging DenseUIS, the work systematically evaluates state-of-the-art deep learning models, uncovering their performance limitations in highly complex and congested urban scenes. By providing a publicly available benchmark with high-quality annotations, DenseUIS fills a significant void in the field and offers a foundational resource to guide future research on urban morphology and infrastructure extraction in informal settlements.

Abstract

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.

Problem

• urban informal settlements
• building recognition
• road recognition
• remote sensing dataset
• dense urban areas

Innovation

• DenseUIS
• urban informal settlements
• building and road extraction
• high-resolution remote sensing
• fine-grained urban mapping