Data collection through censuses is conducted every 10 years on average in Latin America, making it difficult to monitor the growth and support needed by communities living in these settlements. Conducting a field survey requires logistical resources to be able to do it exhaustively. The increasing availability of open data, high-resolution satellite images, and free software to process them allow us to be able to do so in a scalable way based on the analysis of these sources of information. This case study shows the collaboration between Dymaxion Labs and the NGO Techo to employ machine learning techniques to create the first informal settlements census of Tegucigalpa, Honduras.

中文翻译：

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案例研究：利用机器学习绘制特古西加尔巴潜在的非正式定居点区域以规划地面调查

拉丁美洲平均每 10 年通过人口普查收集数据，因此很难监测居住在这些定居点的社区所需的增长和支持。进行实地调查需要后勤资源才能彻底完成。开放数据、高分辨率卫星图像和处理它们的免费软件的可用性不断提高，使我们能够基于对这些信息源的分析，以可扩展的方式进行处理。本案例研究展示了 Dymaxion Labs 与非政府组织 Techo 之间的合作，利用机器学习技术创建了洪都拉斯特古西加尔巴的第一个非正式住区人口普查。