Comparison of spatiotemporal contribution patterns among three crowd-sourcing drone platforms

Abstract

ABSTRACT Since the introduction of drones to the mass market for private users, drone pilots have started to share geo-tagged aerial photos and videos on a variety of drone platforms. This study compares the spatio-temporal contribution patterns of georeferenced drone-based images and videos between three crowd-sourcing platforms, namely Dronestagram, Travelwithdrone, and Flickr. The comparison addresses aspects of spatial accuracy, geographic coverage, contribution inequality, power-law approximations of different contribution characteristics, and negative binomial multilevel regression models that identify man-made and natural features, socio-economic factors, and land use categories that are associated with image and video contribution numbers. This study provides new insight into the abundance of drone-based image and video contributions around the globe, helps to determine which drone platform is best suited to find drone media for a specific location, and discusses a few potential applications that could benefit from crowd-sourced drone imagery and videos.