Icebergs as a Distributed Sensor Network: Iceberg Tracking in Time-Lapse Imagery for Fjord Circulation Analysis

Tidewater glaciers are critical gateways for global sea level rise, with their stability strongly influenced by complex fjord circulation patterns that control submarine melting. Direct observations of these dynamics with conventional oceanographic instruments remain challenging due to temporal or spatial constraints. However, the fjords themselves contain a distributed sensor network: icebergs. As passive tracers driven by currents, icebergs of different sizes respond to circulation at different depths due to their varying underlying drafts. Deriving quantitative circulation data from these tracers requires tracking hundreds to thousands of similar-looking icebergs simultaneously.

This work presents an automated multi-object-tracking framework that extracts dense, size-stratified velocity fields from time-lapse imagery, providing the observational foundation required to reconstruct depth-dependent circulation patterns within glacier fjords. We introduce a scale-adaptive object detection architecture based on Faster R-CNN that achieves 87.1\% detection recall and successfully captures a large fraction of the iceberg population from only a sparse set of manual labels. To maintain persistent identities in dense scenes, we employ a multi-modal association strategy that combines Kalman-filtered motion priors with appearance similarity learned via Vision Transformers. Evaluated across diverse environmental conditions, the framework demonstrates high stability with 95.7\% identity consistency (IDF1) at 2-minute time intervals and generalizes to unseen glaciers without retraining. By transforming time-lapse imagery into quantitative circulation records, this work provides a robust framework for monitoring the hidden ocean dynamics that drive glacier retreat.