3 papers
cs.AI2026
When Big Data Becomes a Curse: Spatial Heterogeneity and the Limits of Learning from Passive Acoustic Monitoring Data
Gabriel Spadon, Wayne Renaud, Priyanka Aravindan
Passive Acoustic Monitoring produces large archives whose recordings are clustered by deployment, season, station identifier, and acquisition configuration. We analyze 908,072 AIS-…
cs.LG2026
Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling
Parth Doshi, Priyanka Aravindan, Vaishnav Vaidheeswaran +2
Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure. For sea s…
cs.SD2026
From Continuous Deployment to Queryable Dataset: Terabyte-Scale AIS-Aligned Passive Acoustic Labelling
Wayne Renaud, Priyanka Aravindan, Gabriel Spadon
Long-duration passive acoustic deployments produce large archives of recordings that are not linked to vessel tracks or encounter structure, leaving range and contact conditions un…