案例研究：利用机器学习绘制特古西加尔巴潜在的非正规住区地图，以规划地面调查
Case study: Mapping potential informal settlements areas in Tegucigalpa with machine learning to plan ground survey
摘要：
在拉丁美洲，通过人口普查收集数据汇集的工作平均每10年进行一次，因此很难监测居住在这些住区的社区的增长和所需的支助。进行实地调查需要后勤资源，以便能够彻底地进行调查。开放数据、高分辨率卫星图像和处理这些数据的免费软件越来越多，使我们能够根据对这些信息来源的分析，以可扩展的方式进行处理。该案例研究显示了Dymaxion Labs和非政府组织Techo之间的合作，采用机器学习技术来创建洪都拉斯特古西加尔巴的第一次非正式定居点普查。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
