利用机器学习与时间序列卫星影像绘制新增非正规住区：委内瑞拉移民危机中的应用
Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis
AI总结：
针对委内瑞拉移民危机，提出用机器学习与哨兵二号时间序列影像快速定位新增非正规住区，经远程与实地两步验证，有效识别2015-2020年哥伦比亚移民定居点。
摘要：
自2014年以来，近200万委内瑞拉人为逃离经济崩溃的国家而涌入哥伦比亚，这构成了现代历史上最大的人道主义危机之一。非政府组织和地方政府单位面临着识别、评估和监测快速增长的移民社区的挑战，以便提供紧急人道主义援助。然而，由于许多流离失所人口居住在全国各地的非正规住区，在大片领土上定位移民定居点可能是一项重大挑战。为解决这一问题，我们提出了一种新颖的方法，利用机器学习和公开可获取的哨兵二号（Sentinel-2）时间序列卫星影像，快速且经济高效地定位新增和新兴的非正规住区。我们展示了该方法在识别2015年至2020年间哥伦比亚境内出现的潜在委内瑞拉移民定居点方面的有效性。最后，我们强调分类后验证的重要性，并提出一种两步验证方法，包括：（1）使用谷歌地球（Google Earth）进行远程验证；（2）通过Premise App（一款移动众包平台）进行实地验证。
英文摘要：
Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history. Non-government organizations and local government units are faced with the challenge of identifying, assessing, and monitoring rapidly growing migrant communities in order to provide urgent humanitarian aid. However, with many of these displaced populations living in informal settlements areas across the country, locating migrant settlements across large territories can be a major challenge. To address this problem, we propose a novel approach for rapidly and cost-effectively locating new and emerging informal settlements using machine learning and publicly accessible Sentinel-2 time-series satellite imagery. We demonstrate the effectiveness of the approach in identifying potential Venezuelan migrant settlements in Colombia that have emerged between 2015 to 2020. Finally, we emphasize the importance of post-classification verification and present a two-step validation approach consisting of (1) remote validation using Google Earth and (2) on-the-ground validation through the Premise App, a mobile crowdsourcing platform.
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