Few months back, we all read the news that Facebook has utilized satellite imagery to generate an estimate of population density over different regions of the Earth. This task was accomplished by Facebook Connectivity Lab, with the goal to identifying possible connectivity options for high population density (urban areas) and low population density (rural areas). These connectivity options can range from Wi-Fi, cellular network, satellite communication, and even laser communication via drones.
Facebook Connectivity Lab found that current population density estimates from censuses are insufficient for this planning purpose, and resolved to make their own high spatial resolution population density estimates from satellite data. What they did was take their computer vision techniques developed for face recognition and photo tagging suggestions in images and applied the same algorithms to analyzing high-resolution satellite imagery (50 cm pixel size) from DigitalGlobe. DigitalGlobe’s Geospatial Big Data platform was made available to Facebook, along with their algorithms for mosaicking and atmospheric correction. The technical methodology employed by DigitalGlobe and Facebook Connectivity Lab, is detailed in this white paper by Facebook. DigitalGlobe’s high resolution satellite data from the past 5 years or so (imagery from high-resolution WorldView and GeoEye satellites), were utilized, and they only used cloud-free visible RGB bands. For cloudy imagery, third party population data was used to fill in the gaps. On this big geospatial dataset from DigitalGlobe, the Facebook team analyzed 20 countries, 21.6 million square km, and 350 TB of imagery using convolutional neural networks. Their final dataset has 5 m resolution, particularly focusing on rural and remote areas, and improves over previous countrywide population density estimates by multiple orders of magnitude.