Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring

Abstract Short-term monitoring of alpine glaciers is crucial to understand their response to climate change. This paper presents a low-cost multi-camera system tailored for 4D glacier monitoring using deep learning stereo-photogrammetry. Our approach integrates multi-temporal 3D reconstruction from stereo cameras and surface velocity estimation from a monoscopic camera through digital image correlation. To address the challenges posed by wide camera baselines in complex environments, we have integrated state-of-the-art deep learning feature matching algorithms into ICEpy4D, a Python toolkit designed for 4D monitoring (https: //github. com/franioli/icepy4d). In a pilot study conducted on the debris-covered Belvedere Glacier (Italian Alps), our stereoscopic setup, with a camera base–height ratio close to one, captured daily images from May to November 2022. Our approach utilized SuperPoint and SuperGlue for feature matching, resulting in a daily 3D reconstruction of the glacier terminus, as traditional SIFT-like feature matching fails in this scenario. Using dense point clouds with decimetric accuracy, we estimated daily ice volume loss and glacier retreat at the terminus. The total ice volume loss was 63\, 000\, m 63 000 m ^3 3 and the retreat was 17. 8\, m 17. 8 m. Surface kinematics revealed three times higher surface velocity during the warm season (May–September) than in the fall (September–November). Daily analyses revealed a significant short-term correlation between air temperature, glacier surface velocity and ice ablation, providing insight into the glacier’s response to external forces. The low cost and ease of deployment of the proposed system facilitates replication at other sites for short-term monitoring of glacier dynamics.