3D Spatial Coverage Measurement of Aerial Images

Unmanned aerial vehicles (UAVs) such as drones are becoming significantly prevalent in both daily life (e.g., event coverage, tourism) and critical situations (e.g., disaster management, military operations), generating an unprecedented number of aerial images and videos. UAVs are usually equipped with various sensors (e.g., GPS, accelerometers and gyroscopes) so provide sufficient spatial metadata that describe the spatial extent (referred to as aerial field-of-view) of recorded imagery. Such spatial metadata can be used efficiently to answer a fundamental question about how well a collection of aerial imagery covers a certain area spatially by evaluating the adequacy of the collected aerial imagery and estimating their sufficiency. This paper provides an answer to such questions by introducing 3D spatial coverage measurement models to collectively quantify the spatial and directional coverage of a geo-tagged aerial image dataset. Through experimental evaluation using real datasets, the paper demonstrates that our proposed models can be implemented with highly efficient computation of 3D space geometry.