Searching Sage: AI-Powered Image Retrieval on a Nationwide Edge Computing Cyberinfrastructure

The Sage project operates a national-scale distributed cyberinfrastructure of AIenabled edge computing platforms connected to a wide range of sensors. AI algorithms running on Sage can analyze images, audio, LIDAR, and meteorological data to understand ecosystems, urban activity, and detect wildfires. With over 33 million images collected, there is a critical need for tools that enable fast, meaningful exploration of massive visual datasets. Traditional metadata-based methods (e.g., tags or timestamps) fall short for domain-specific retrieval at this scale. We present a modular, end-to-end image search system designed for distributed sensor networks. The workflow integrates automated captioning (Gemma-3), semantic embeddings (DFN5B-CLIP), keyword indexing (BM25), and reranking (ms-marco-MiniLM-L6-v2), all backed by a scalable Weaviate vector database. Images and their generated captions are embedded into a unified semantic space, enabling hybrid search that combines both semantic similarity and keyword relevance when users submit natural language queries. This architecture supports both real-time monitoring and historical analysis, scaling efficiently with large datasets and user demand. Although demonstrated on Sage, the system is model-agnostic and infrastructure-flexible, making it applicable to other distributed sensor networks. It empowers researchers in fields such as ecology and atmospheric science to search image collections by content-not just metadata-enhancing access to sensor data for scientific analysis and decision-making.