English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The rapid development of remote sensing techniques provides rich,large-coverage, and high-temporal information of the ground, which can becoupled with the emerging deep learning approaches that enable latent featuresand hidden geographical patterns to be extracted. This study marks the firstattempt to cross-compare performances of popular state-of-the-art deep learningmodels in estimating population distribution from remote sensing images,investigate the contribution of neighboring effect, and explore the potentialsystematic population estimation biases. We conduct an end-to-end training offour popular deep learning architectures, i.e., VGG, ResNet, Xception, andDenseNet, by establishing a mapping between Sentinel-2 image patches and theircorresponding population count from the LandScan population grid. The resultsreveal that DenseNet outperforms the other three models, while VGG has theworst performances in all evaluating metrics under all selected neighboringscenarios. As for the neighboring effect, contradicting existing studies, ourresults suggest that the increase of neighboring sizes leads to reducedpopulation estimation performance, which is found universal for all fourselected models in all evaluating metrics. In addition, there exists a notable,universal bias that all selected deep learning models tend to overestimatesparsely populated image patches and underestimate densely populated imagepatches, regardless of neighboring sizes. The methodological, experimental, andcontextual knowledge this study provides is expected to benefit a wide range offuture studies that estimate population distribution via remote sensingimagery.

Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases