A deep residual network implementation for satellite-derived altimetry identification and classification

ICESat-2's Advanced Terrain Laser Altimeter System (ATLAS) can penetrate water bodies, enabling accurate and detailed measurements of bathymetry in diverse aquatic environments. ATLAS's capabilities have made it a popular tool for understanding underwater topography and characteristics. In this paper, we present a deep residual classification network used to identify ICESat-2 bathymetry and water surface photons. The training data used to train the model was derived from both hand-labeled ICESat-2 groundtracks and from synthetic data produced by custom ICESat-2 ground track simulator software. This investigation was unique in that it used a very wide variety of ground tracks across the entire globe, and it also used several different metrics to summarize the classification performance.