The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critical situations, Earth observation (EO) data can play a vital role in timely decision making and can save many people’s lives. This work provides an overview of the potential of EO-based global and local datasets, as well as local data gathering procedures (e.g., drones), in support of COVID-19 responses by referring to two slum areas in Salvador, Brazil as a case study. We discuss the role of datasets as well as data gaps that hinder COVID-19 responses. In Salvador and other low- and middle-income countries’ (LMICs) cities, local data are available; however, they are not up to date. For example, depending on the source, the population of the study areas in 2020 varies by more than 20%. Thus, EO data integration can help in updating local datasets and in the acquisition of physical parameters of poor urban communities, which are often not systematically collected in local surveys.

中文翻译：

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COVID-19的空间维度：来自巴西的证据支持贫民窟社区的地球观测数据的潜力

COVID-19突发卫生事件正在影响我们的全部生活，但是与正式城市相比，贫困社区中发现的生活条件和城市形态使居民更容易受到COVID-19爆发的影响，在正式城市中，居民有资源遵循世卫组织的准则。一般而言，市政空间数据集不能很好地支持对突发卫生事件的空间响应，尤其是在贫困社区中。在这种紧急情况下，对地观测（EO）数据可以在及时决策中发挥至关重要的作用，并可以挽救许多人的生命。通过参考巴西萨尔瓦多的两个贫民区，这项工作概述了基于EO的全球和本地数据集的潜力以及本地数据收集程序（例如，无人机），以支持COVID-19响应。研究。我们讨论了数据集的作用以及阻碍COVID-19响应的数据缺口。在萨尔瓦多和其他中低收入国家（LMIC）的城市，可以获得本地数据。但是，它们不是最新的。例如，根据来源，2020年研究区域的人口差异超过20％。因此，EO数据集成可以帮助更新本地数据集和获取贫困城市社区的物理参数，而这些参数通常在本地调查中没有被系统地收集。