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<p>Automated delineation of settlements at the level of single buildings is advancing on global scale due to developments in image processing techniques. However, in morphologically complex poverty areas (e.g., slums or informal settlements) automated image classification reaches limits. This gap of systematic, accurate geodata hinders the impetus of ‘better data for better decisions’ by the UN Sustainable Development Goals. To bridge this gap, we present a dataset of &gt;320,000 building footprints derived from satellite imagery in 44 poverty areas across the globe. We use ‘Manual Visual Image Interpretation’ for consistent delineations of rooftops proxying building footprints. The method has been applied to 1. <i>classify</i> different <i>morphological types</i> representing poor living environments based on spatial features; 2. <i>document multitemporal</i> dynamics at different spatial scales; and, 3. <i>assess interpreter-related uncertainties</i> across interpreters. We release these data along with interpretation guidelines and validation. We provide a robust empirical basis for the global systematization and comparative analysis of settlement forms proxying poverty, while offering training and validation data for automated image classification.</p>

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A manually interpreted geo-dataset of building footprints in complex urban poverty areas across the globe

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

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  • Nicolas J. Kraff,
  • Michael Wurm,
  • John Friesen,
  • Henri Debray,
  • Hannes Taubenböck

摘要

Automated delineation of settlements at the level of single buildings is advancing on global scale due to developments in image processing techniques. However, in morphologically complex poverty areas (e.g., slums or informal settlements) automated image classification reaches limits. This gap of systematic, accurate geodata hinders the impetus of ‘better data for better decisions’ by the UN Sustainable Development Goals. To bridge this gap, we present a dataset of >320,000 building footprints derived from satellite imagery in 44 poverty areas across the globe. We use ‘Manual Visual Image Interpretation’ for consistent delineations of rooftops proxying building footprints. The method has been applied to 1. classify different morphological types representing poor living environments based on spatial features; 2. document multitemporal dynamics at different spatial scales; and, 3. assess interpreter-related uncertainties across interpreters. We release these data along with interpretation guidelines and validation. We provide a robust empirical basis for the global systematization and comparative analysis of settlement forms proxying poverty, while offering training and validation data for automated image classification.