Geolocating Earth Imagery from ISS: Integrating Machine Learning with Astronaut Photography for Enhanced Geographic Mapping
This work addresses the challenge of geolocating ISS imagery for remote sensing and Earth observation applications, representing an incremental improvement by applying existing methods to a specific domain problem.
This paper tackles the problem of identifying specific Earth locations in astronaut-taken photographs from the International Space Station (ISS) by employing three machine learning pipelines (Neural Network, SIFT, and GPT-4), achieving varied success rates in automated geolocation across a dataset of over 140 ISS images.
This paper presents a novel approach to geolocating images captured from the International Space Station (ISS) using advanced machine learning algorithms. Despite having precise ISS coordinates, the specific Earth locations depicted in astronaut-taken photographs often remain unidentified. Our research addresses this gap by employing three distinct image processing pipelines: a Neural Network based approach, a SIFT based method, and GPT-4 model. Each pipeline is tailored to process high-resolution ISS imagery, identifying both natural and man-made geographical features. Through extensive evaluation on a diverse dataset of over 140 ISS images, our methods demonstrate significant promise in automated geolocation with varied levels of success. The NN approach showed a high success rate in accurately matching geographical features, while the SIFT pipeline excelled in processing zoomed-in images. GPT-4 model provided enriched geographical descriptions alongside location predictions. This research contributes to the fields of remote sensing and Earth observation by enhancing the accuracy and efficiency of geolocating space-based imagery, thereby aiding environmental monitoring and global mapping efforts.