Sentinel-2 Image Retrieval with Global, Cross-modal Embeddings

Recent advancements in Earth embeddings have opened up new frontiers for geosciences, enabling efficient analysis of vast volumes of geospatial data. However, the practical utilization of these embeddings is often hindered by complex software environments and the requirement for specialized computational expertise. To help democratize access to Earth embeddings, we introduce EarthEmbeddingExplorer, an open-source, web-based application designed to enhance the accessibility, understanding and interactivity of Earth embeddings for the broader geoscience community. 

EarthEmbeddingExplorer integrates multiple state-of-the-art foundation models, including SatCLIP, FarSLIP, and SigLIP, to support cross-modal retrieval of Sentinel-2 imagery via text, image, and geographic location queries. Our implementation leverages the MajorTOM Core-S2L2A dataset as the primary data source; we pre-computed approximately 250,000 embeddings per model based on a uniform spatial sampling of the MajorTOM grid. This approach ensures a representative global coverage of 1.2% of the Earth's land surface. To ensure accessibility, all models and datasets are hosted on open-source frameworks, specifically ModelScope and Hugging Face. The application provides an intuitive interface for visualizing the geographical distribution of the retrieved results, rendering top-match thumbnails, and exporting comprehensive metadata. Such transparent and low-cost access to large-scale embedding analysis is essential for identifying model-specific advantages and limitations. By enabling instant cross-model comparisons within specific spatiotemporal contexts, EarthEmbeddingExplorer allows users to evaluate model performance for their unique monitoring needs and domains of interest.

Ongoing development focuses on expanding EarthEmbeddingExplorer’s capabilities by integrating additional embedding models such as DINOv2, and increasing global spatial coverage. We are further implementing FAISS-based vector similarity search to enable near-instantaneous queries across tens of millions of global embeddings. Future iterations will prioritize modular software architecture, standardized APIs, and detailed documentation to facilitate community-driven contributions of new embedding models and datasets. The web applications are accessible at https://huggingface.co/spaces/ML4Sustain/EarthExplorer and at https://www.modelscope.cn/studios/VoyagerX/EarthExplorer.