Satellite-Based Mapping of Urban Poverty With Transfer-Learned Slum Morphologies

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 $89\%$.