  The rapid development of remote sensing techniques provides rich,
large-coverage, and high-temporal information of the ground, which can be
coupled with the emerging deep learning approaches that enable latent features
and hidden geographical patterns to be extracted. This study marks the first
attempt to cross-compare performances of popular state-of-the-art deep learning
models in estimating population distribution from remote sensing images,
investigate the contribution of neighboring effect, and explore the potential
systematic population estimation biases. We conduct an end-to-end training of
four popular deep learning architectures, i.e., VGG, ResNet, Xception, and
DenseNet, by establishing a mapping between Sentinel-2 image patches and their
corresponding population count from the LandScan population grid. The results
reveal that DenseNet outperforms the other three models, while VGG has the
worst performances in all evaluating metrics under all selected neighboring
scenarios. As for the neighboring effect, contradicting existing studies, our
results suggest that the increase of neighboring sizes leads to reduced
population estimation performance, which is found universal for all four
selected models in all evaluating metrics. In addition, there exists a notable,
universal bias that all selected deep learning models tend to overestimate
sparsely populated image patches and underestimate densely populated image
patches, regardless of neighboring sizes. The methodological, experimental, and
contextual knowledge this study provides is expected to benefit a wide range of
future studies that estimate population distribution via remote sensing
imagery.
