We propose a neural network component, the regional aggregation layer, that
makes it possible to train a pixel-level density estimator using only
coarse-grained density aggregates, which reflect the number of objects in an
image region. Our approach is simple to use and does not require
domain-specific assumptions about the nature of the density function. We
evaluate our approach on several synthetic datasets. In addition, we use this
approach to learn to estimate high-resolution population and housing density
from satellite imagery. In all cases, we find that our approach results in
better density estimates than a commonly used baseline. We also show how our
housing density estimator can be used to classify buildings as residential or
non-residential.

本文提出了一种基于神经网络的区域聚合层，可使用粗粒度的密度聚合来训练像素级密度估计器，从而反映图像区域中对象的数量，使该方法不需要针对密度函数的领域特定假设。我们在几个合成数据集上评估了我们的方法。此外，我们使用该方法从卫星图像中学习估计高分辨率人口和住房密度，并且在所有情况下，我们发现与常用基线相比，我们的方法都能得到更好的密度估计值。我们还展示了如何使用我们的住房密度估计器将建筑分类为住宅或非住宅。