Demonstrating EarthLake: A Model Lake System for Earth Observation Foundation Model Management
Foundation models (FMs) and their task-specific variants are increasingly forming large and heterogeneous collections that resemble model lakes. Managing such lakes requires more than hosting models: users must be able to discover relevant models, compare them using reproducible evidence, and operationalize selected models for downstream use. We present EARTHLAKE, a model lake system for Earth Observation (EO) FMs. The system integrates a schema-guided model registry, task-driven model discovery, and an experiment management framework for benchmarking and comparing candidate FMs. In the demo, participants explore the model registry, discover FMs using natural language task descriptions, benchmark selected FMs on EO datasets, compare experiment results, and execute chosen FMs on new imagery. Our interactive demonstration illustrates how EARTHLAKE supports end-to-end workflows for discovering, evaluating, and operating EO FMs.
