Accurate and up-to-date mapping of the human population is fundamental for a wide range of disciplines, from effective governance and establishing policies to disaster management and crisis dilution. The traditional method of gathering population data through census is costly and time-consuming. Recently, with the availability of large amounts of Earth observation data sets, deep learning methods have been explored for population estimation; however, they are either limited by census data availability, inter-regional evaluations, or transparency. In this paper, we present an end-to-end interpretable deep learning framework for large-scale population estimation at a resolution of 1km that uses only the publicly available data sets and does not rely on census data for inference. The architecture is based on a modification of the common ResNet-50 architecture tailored to analyze both image-like and vector-like data. Our best model outperforms the baseline random forest model by improving the RMSE by around 9% and also surpasses the community standard product, GHS-POP, thus yielding promising results. Furthermore, we improve the transparency of the proposed model by employing an explainable AI technique that identified land use information to be the most relevant feature for population estimation. We expect the improved interpretation of the model outcome will inspire both academic and non-academic end users, particularly those investigating urbanization or sub-urbanization trends, to have confidence in the deep learning methods for population estimation.

中文翻译：

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使用地球观测数据进行一致的大规模城市人口估计的可解释深度学习

准确和最新的人口测绘对于从有效治理和制定政策到灾害管理和危机淡化等广泛学科而言至关重要。通过人口普查收集人口数据的传统方法既昂贵又耗时。近年来，随着大量地球观测数据集的出现，深度学习方法被探索用于人口估计。然而，它们要么受到人口普查数据可用性、区域间评估或透明度的限制。在本文中，我们提出了一种端到端的可解释深度学习框架，用于分辨率为 1 公里的大规模人口估计，该框架仅使用公开可用的数据集，不依赖于人口普查数据进行推理。该架构基于通用 ResNet-50 架构的修改，专门用于分析类图像和类矢量数据。我们的最佳模型通过将 RMSE 提高了约 9% 来超越基线随机森林模型，并且还超过了社区标准产品 GHS-POP，从而产生了有希望的结果。此外，我们通过采用可解释的人工智能技术来提高所提出模型的透明度，该技术将土地利用信息确定为人口估计最相关的特征。我们预计对模型结果的改进解释将激励学术和非学术最终用户，特别是那些研究城市化或郊区化趋势的用户，对人口估计的深度学习方法充满信心。