EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images

Yijie Zheng; Weijie Wu; Zhuoyao He; Bingyue Wu; Long Zhao; Guoqing Li; Mikolaj Czerkawski; Konstantin Klemmer

cs.CVMar 31, 2026

EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images

Authors: Yijie Zheng, Weijie Wu, Zhuoyao He, Bingyue Wu, Long Zhao, Guoqing Li, Mikolaj Czerkawski, Konstantin Klemmer

Organizations: Aerospace Information Research Institute, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences · Asterisk Labs ·

Abstract

While the Earth observation community has witnessed a surge in high-impact foundation models and global Earth embedding datasets, a significant barrier remains in translating these academic assets into freely accessible tools. This tutorial introduces EarthEmbeddingExplorer, an interactive web application designed to bridge this gap, transforming static research artifacts into dynamic, practical workflows for discovery. We will provide a comprehensive hands-on guide to the system, detailing its cloud-native software architecture, demonstrating cross-modal queries (natural language, visual, and geolocation), and showcasing how to derive scientific insights from retrieval results. By democratizing access to precomputed Earth embeddings, this tutorial empowers researchers to seamlessly transition from state-of-the-art models and data archives to real-world application and analysis. The web application is available at https://modelscope.ai/studios/Major-TOM/EarthEmbeddingExplorer.

Figures & tables

Model / variant	Training data	Encoder input	Dim.
FarSLIP (ViT-B/16)	RS5M, MGRS-200K	224 $\times$ 224 RGB	512
Qwen3-VL-Embedding-2B	Synthetic + curated multimodal	Dynamic RGB	2,048
SigLIP (ViT-SO400M/14)	WebLI	384 $\times$ 384 RGB	1,152
TIPSv2 (ViT-B/14)	WebLI-116M + synthetic	448 $\times$ 448 RGB	768
SatCLIP (ViT16-L40)	S2-100K	224 $\times$ 224, 12-band S2	256
DINOv2-large (ViT-L/14)	LVD-142M	224 $\times$ 224 RGB	1,024

Table 1: Embedding models used in EarthEmbeddingExplorer. Model input denotes the resolution and spectral channels supplied to the encoder. Clay and OlmoEarth additionally use acquisition metadata, while Qwen3-VL-Embedding supports dynamic-resolution RGB inputs and native mixed text–image queries. Query modalities are denoted by text, image, and geolocation.

Figure 1: A cloud-native retrieval pipeline based on ModelScope Studio.

Figure 2: EarthEmbeddingExplorer user interface.

Panel of Figure 3 — Figure 3: Geographic distribution of retrieved matches under a top-2.5% threshold for different models and query modalities.

Panel of Figure 4 — Figure 4: Top-5 retrieved tiles for the same case study in Figure 3 .

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Geographical distribution of sampled grids

Panel of Figure 6 — Figure 6: Cross-model text-to-image retrieval for a satellite image of a slum . The first two rows show global distributions, and the last two rows show top-5 retrieved tiles.

Panel of Figure 7 — Figure 7: Cross-model text-to-image retrieval for a satellite image of snow covered mountains . The first two rows show global distributions, and the last two rows show top-5 retrieved tiles.

Panel of Figure 8 — Figure 8: Cross-model text-to-image retrieval for a satellite image of a glacier . The first two rows show global distributions, and the last two rows show top-5 retrieved tiles.

Panel of Figure 9 — Figure 9: Image-to-image retrieval for a center-pivot farmland query across five RGB-based and three multispectral models. Each panel repeats the query at left and shows the model’s top-5 results at right.

Panel of Figure 10 — Figure 10: Image-to-image retrieval for a hand-drawn center-pivot-field sketch across five models. Each panel repeats the sketch at left and shows the model’s top-5 results at right.

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Remote sensing uses satellite, aerial, and ground-based observations to measure and map Earth and other planetary surfaces.

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Earth Embeddings

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Earth observation is moving from foundation models that users must run themselves toward embedding products that package model feature outputs as reusable data without needing to download and process the imagery used to generate them. Earth embeddings are vectors that summarize locations, image patches, or pixels, letting users analyze compact features instead of repeatedly training or running large models on raw satellite imagery. This chapter explains the main types of Earth embeddings, from implicit location encoders to explicit patch and pixel products, and compares their coverage, resolution, dimensionality, storage cost, licenses, and reproducibility. We review their use in land cover and crop mapping, ecological and hazard modeling, socioeconomic prediction, and semantic search, with evidence on when embeddings improve on conventional features and when pooling, fusion, or spatial transfer limit performance. Two case studies show practical workflows for similarity search and land cover mapping. We close with guidance for choosing, evaluating, storing, compressing, and publishing embeddings, and with open problems in oceanic and atmospheric coverage, uncertainty, and benchmarking.

Abstract

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