In the course of global urbanization, poverty in cities has been observed to increase, especially in the Global South. Poverty is one of the major challenges for our society in the upcoming decades, making it one of the most important issues in the Sustainable Development Goals defined by the United Nations. Satellite-based mapping can provide valuable information about slums where insights about the location and size are still missing. Large-scale slum mapping remains a challenge, fuzzy feature spaces between formal and informal settlements, significant imbalance of slum occurrences opposed to formal settlements, and various categories of multiple morphological slum features. We propose a transfer learned fully convolutional Xception network (XFCN), which is able to differentiate between formal built-up structures and the various categories of slums in high-resolution satellite data. The XFCN is trained on a large sample of globally distributed slums, located in cities of Cape Town, Caracas, Delhi, Lagos, Medellin, Mumbai, Nairobi, Rio de Janeiro, São Paulo, and Shenzhen. Slums in these cities are greatly heterogeneous in its morphological feature space and differ to a varying degree to formal settlements. Transfer learning can help to improve segmentation results when learning on a variety of slum morphologies, with high $F$1 scores of up to $89\%$.

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基于卫星的城市贫困地图与迁移学习的贫民窟形态

在全球城市化进程中，观察到城市贫困在增加，特别是在全球南方。贫困是未来几十年我们社会面临的主要挑战之一，使其成为联合国定义的可持续发展目标中最重要的问题之一。基于卫星的测绘可以提供关于贫民窟的有价值的信息，而贫民窟的位置和规模仍然缺乏洞察力。大规模的贫民窟制图仍然是一个挑战，正式和非正式住区之间的特征空间模糊，与正式住区相对的贫民窟出现显着不平衡，以及多种形态的贫民窟特征类别。我们提出了一个转移学习的全卷积 Xception 网络（XFCN），它能够在高分辨率卫星数据中区分正式的建筑结构和各种类型的贫民窟。XFCN 在全球分布的大量贫民窟样本上进行了培训，这些贫民窟位于开普敦、加拉加斯、德里、拉各斯、麦德林、孟买、内罗毕、里约热内卢、圣保罗和深圳等城市。这些城市中的贫民窟在其形态特征空间上存在很大差异，与正式聚居区的差异程度也不同。在学习各种贫民窟形态时，迁移学习可以帮助提高分割结果，$F$1 分数高达 $89\%$。这些城市中的贫民窟在形态特征空间上存在很大差异，与正式聚居区的差异程度也不同。在学习各种贫民窟形态时，迁移学习可以帮助提高分割结果，$F$1 分数高达 $89\%$。这些城市中的贫民窟在其形态特征空间上存在很大差异，与正式聚居区的差异程度也不同。在学习各种贫民窟形态时，迁移学习可以帮助提高分割结果，$F$1 分数高达 $89\%$。