论文标题
估计开销流离失所
Estimating Displaced Populations from Overhead
论文作者
论文摘要
我们介绍了一种深度学习方法，以使用高分辨率开销图像对位移营地进行细粒度的估计。我们在2018年和2019年对无人机图像进行了训练和评估我们的方法与孟加拉国Cox Bazar的难民营的人口数据交叉引用。我们提出的方法在隔离营地图像中达到7.02％的绝对百分比误差。我们认为，我们使用现实世界流离失所的camp数据进行的实验构成了开发工具的重要一步，使人道主义社区能够有效，迅速地应对全球流离失所危机。
We introduce a deep learning approach to perform fine-grained population estimation for displacement camps using high-resolution overhead imagery. We train and evaluate our approach on drone imagery cross-referenced with population data for refugee camps in Cox's Bazar, Bangladesh in 2018 and 2019. Our proposed approach achieves 7.02% mean absolute percent error on sequestered camp imagery. We believe our experiments with real-world displacement camp data constitute an important step towards the development of tools that enable the humanitarian community to effectively and rapidly respond to the global displacement crisis.
