Metzler A Barbara, Nathvani Ricky, Sharmanska Viktoriia, Bai Wenjia, Moulds Simon, Owoo Nkechi Srodah, Fynn Iris Ekua Mensimah, Muller Emily, Dufitimana Esaie, Akara Ghafi Kondi, Owusu George, Agyei-Mensah Samuel, Ezzati Majid
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, Imperial College London, London, UK.
Department of Informatics, University of Sussex, UK; Department of Computing, Imperial College London, London, UK.
Sci Total Environ. 2025 Aug 1;988:179739. doi: 10.1016/j.scitotenv.2025.179739. Epub 2025 Jun 5.
Sub-Saharan Africa and other developing regions have urbanized extensively, leading to complex urban features with varying presence and types of roads, buildings and vegetation. We use a novel hierarchical deep learning framework and high-resolution satellite images to characterize multidimensional urban environments in multiple cities. Application of the model to images from Accra, Dakar, and Dar es Salaam identified areas with analogous patterns of building density, roads and vegetation. These included dense settlements within the metropolitan boundary (20-54% of urban area), peri-urban intermix of natural and built environment (21-44%), natural vegetation (9-13%) and agricultural land (8-15%). Kigali, with its mountainous geography and post-colonial expansion, exhibited unique urban characteristics including a sparser urban core (23%) and significant wildland-urban intermix (19% of vegetation). Other notable clusters were water (2% of area of Accra) and empty land (8-10% of Accra and Dakar). Our results demonstrate that unlabeled satellite images with unsupervised deep learning can be used for consistent and coherent near-real-time urban monitoring, particularly in regions where traditional data are scarce.
撒哈拉以南非洲和其他发展中地区已广泛城市化，形成了具有不同道路、建筑物和植被分布及类型的复杂城市特征。我们使用一种新颖的分层深度学习框架和高分辨率卫星图像来描述多个城市的多维城市环境。将该模型应用于阿克拉、达喀尔和达累斯萨拉姆的图像，识别出了建筑密度、道路和植被模式相似的区域。这些区域包括大都市区边界内的密集居住区（占城市面积的20 - 54%）、自然与建成环境的城郊混合区（21 - 44%）、自然植被（9 - 13%）和农业用地（8 - 15%）。基加利因其多山的地理环境和后殖民时期的扩张，呈现出独特的城市特征，包括较为稀疏的城市核心区（23%）和显著的城乡交错带（植被占19%）。其他显著的区域类别包括水域（占阿克拉面积的2%）和空地（占阿克拉和达喀尔面积的8 - 10%）。我们的结果表明，利用无监督深度学习的未标记卫星图像可用于进行连贯一致的近实时城市监测，特别是在传统数据稀缺的地区。
