Since the United Nations launched the Sustainable Development Goals (SDG) in 2015, numerous universities, NGOs and other organizations have attempted to develop tools for monitoring worldwide progress in achieving them. Led by advancements in the fields of earth observation techniques, data sciences and the emergence of artificial intelligence, a number of research teams have developed innovative tools for highlighting areas of vulnerability and tracking the implementation of SDG targets. In this paper we demonstrate that individuals with no organizational affiliation and equipped only with common hardware, publicly available datasets and cloud-based computing services can participate in the improvement of predicting machine-learning-based approaches to predicting local poverty levels in a given agro-ecological environment. The approach builds upon several pioneering efforts over the last five years related to mapping poverty by deep learning to process satellite imagery and "ground-truth" data from the field to link features with incidence of poverty in a particular context. The approach employs new methods for object identification in order to optimize the modeled results and achieve significantly high accuracy. A key goal of the project was to intentionally keep costs as low as possible - by using freely available resources - so that citizen scientists, students and organizations could replicate the method in other areas of interest. Moreover, for simplicity, the input data used were derived from just a handful of sources (involving only earth observation and population headcounts). The results of the project could therefore certainly be strengthened further through the integration of proprietary data from social networks, mobile phone providers, and other sources.

中文翻译：

---

从太空看贫困，能调到多少？

自联合国于 2015 年启动可持续发展目标 (SDG) 以来，许多大学、非政府组织和其他组织都试图开发工具来监测全球实现这些目标的进展情况。在地球观测技术、数据科学和人工智能领域的进步的引领下，许多研究团队开发了创新工具来突出脆弱领域并跟踪可持续发展目标的实施情况。在本文中，我们证明没有组织隶属关系且仅配备通用硬件、公开可用的数据集和基于云的计算服务的个人可以参与改进基于机器学习的预测方法，以预测给定农业中的当地贫困水平。生态环境。该方法建立在过去五年中与通过深度学习处理卫星图像和实地“地面实况”数据以将特征与特定背景下的贫困发生率联系起来的贫困测绘相关的多项开创性努力的基础上。该方法采用新的对象识别方法，以优化建模结果并实现显着的高精度。该项目的一个关键目标是有意将成本保持在尽可能低的水平——通过使用免费可用的资源——以便公民科学家、学生和组织可以在其他感兴趣的领域复制该方法。此外，为简单起见，所使用的输入数据仅来自少数来源（仅涉及地球观测和人口统计）。