论文标题
细粒度的细粒度映射和开放的Geodata映射
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
论文作者
论文摘要
在几个领域，例如城市规划，环境监测，公共卫生和人道主义行动，都需要细粒度的人口图。不幸的是，在许多国家 /地区，只收集了大型空间单位的人口普查计数，而且这些并不总是最新的。我们提出了Pomelo，这是一种深入学习模型，该模型采用粗糙的人口普查计数和开放的地理量，以估计具有100m地面采样距离的细粒度图。此外，当根本没有普查计数时，该模型还可以通过在各个国家概括。在撒哈拉以南非洲以下几个国家的一系列实验中，与最详细的可用参考数量相吻合的图：粗被人口普查计数的分类达到85-89％；在没有任何计数的情况下，不受约束的预测达到48-69％。
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with 100m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELOare in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches R2 values of 85-89%; unconstrained prediction in the absence of any counts reaches 48-69%.
