Automated georeferencing of nighttime ISS timelapses using synthetic Earth renders, deep feature matching, and VIIRS reference data

Nighttime photographs acquired from the International Space Station (ISS) represent a unique resource for analysing light pollution, urban structure and nocturnal Earth dynamics. However, the lack of camera orientation metadata and the complex acquisition geometry have historically limited their scientific use. This work presents an automated pipeline for georeferencing ISS nighttime timelapses by combining physically based simulations of the ISS orbit, deep-learning feature matching between real and synthetic imagery, and refinement with VIIRS DNB satellite products. First, a global VIIRS nighttime lights texture, ISS TLE data and the camera model are used in Blender to generate synthetic views matching the time span and geometry of the real timelapse. Deep feature matching between each real frame and its corresponding synthetic render provides image-to-ground correspondences, which are projected into geographic coordinates and used as ground control points for a first georeferencing. In an optional refinement stage, georeferenced ISS frames are locally aligned to VIIRS subsets through optical flow, and the resulting displacement fields are used to correct the control points and perform a second georeferencing. The resulting workflow enables large-scale processing of ISS datasets and provides georeferenced nighttime maps suitable for ecological analysis, urban monitoring and light pollution research.