100m population grid in CONUS from Microsoft building footprints
This study presents a 100m population grid in CONUS, disaggregated from the ACS 5-year estimates (2013-2017) using 125 million building footprints released by Microsoft. Land use dataset from OSM, a crowdsourced platform, was applied to trim the raw footprints. Layers derived from trimmed footprint statistics were considered as weighting scenarios for the dasymatric method, which was further applied to disaggregate the ACS census tract estimates into the 100m population grid.
Population Distribution from Satellite Imagery Via Deep Learning
This article 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.
Translating Multispectral Imagery to Nighttime Imagery via Conditional Generative Adversarial Networks
Nighttime satellite imagery has been applied in a wide range of fields. However, our limited understanding of how observed light intensity is formed and whether it can be simulated greatly hinders its further application. This study explores the potential of conditional Generative Adversarial Networks (cGAN) in translating multispectral imagery to nighttime imagery. A popular cGAN framework, pix2pix, was adopted and modified to facilitate this translation using gridded training image pairs derived from Landsat 8 and Visible Infrared Imaging Radiometer Suite (VIIRS). The results of this study prove the possibility of multispectral-tonighttime translation and further indicate that, with the additional social media data, the generated nighttime imagery can be very similar to the ground-truth imagery. This study fills the gap in understanding the composition of satellite observed nighttime light and provides new paradigms to solve the emerging problems in nighttime remote sensing fields, including nighttime series construction, light desaturation, and multi-sensor calibration