Selected work

Representative Papers

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

Mar 31, 2026

This work addresses the challenge of transforming Earth observation foundation models and their associated embeddings into open, accessible scientific tools that support cross-modal retrieval and discovery. The authors present a cloud-native, interactive web platform that, for the first time, publicly provides precomputed embeddings for global satellite imagery. The system integrates three complementary query modalities—natural language, image similarity, and geographic coordinates—enabling efficient, low-barrier, multimodal search over remote sensing data. By bridging the gap between advanced academic models and real-world applications, the platform significantly enhances the accessibility and scientific utility of Earth observation data at a global scale.

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SITS-DECO: A Generative Decoder Is All You Need For Multitask Satellite Image Time Series Modelling

Oct 21, 2025

Existing remote sensing foundation models often rely on specific data sources or training paradigms, limiting their generalizability and flexibility across downstream tasks. To address this, we propose SITS-DECO—the first pure generative decoder-based foundation model tailored for Satellite Image Time Series (SITS). It employs symbolic prompting to unify multi-temporal, multi-modal remote sensing inputs as discrete token sequences, eliminating the need for task- or modality-specific architectural design. Crucially, SITS-DECO discards spatial convolutions and encoder components, focusing exclusively on dense temporal dynamics modeling. Evaluated on the PASTIS-R crop classification benchmark, it achieves superior performance over state-of-the-art remote sensing large models despite significantly fewer parameters. This demonstrates the effectiveness and scalability of the “lightweight + time-centric + symbolic sequence” paradigm for multi-task Earth observation.

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Recent publications

Latest Papers

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

Mar 31, 2026

This work addresses the challenge of transforming Earth observation foundation models and their associated embeddings into open, accessible scientific tools that support cross-modal retrieval and discovery. The authors present a cloud-native, interactive web platform that, for the first time, publicly provides precomputed embeddings for global satellite imagery. The system integrates three complementary query modalities—natural language, image similarity, and geographic coordinates—enabling efficient, low-barrier, multimodal search over remote sensing data. By bridging the gap between advanced academic models and real-world applications, the platform significantly enhances the accessibility and scientific utility of Earth observation data at a global scale.

0 citationsRead paper

SITS-DECO: A Generative Decoder Is All You Need For Multitask Satellite Image Time Series Modelling

Oct 21, 2025

Existing remote sensing foundation models often rely on specific data sources or training paradigms, limiting their generalizability and flexibility across downstream tasks. To address this, we propose SITS-DECO—the first pure generative decoder-based foundation model tailored for Satellite Image Time Series (SITS). It employs symbolic prompting to unify multi-temporal, multi-modal remote sensing inputs as discrete token sequences, eliminating the need for task- or modality-specific architectural design. Crucially, SITS-DECO discards spatial convolutions and encoder components, focusing exclusively on dense temporal dynamics modeling. Evaluated on the PASTIS-R crop classification benchmark, it achieves superior performance over state-of-the-art remote sensing large models despite significantly fewer parameters. This demonstrates the effectiveness and scalability of the “lightweight + time-centric + symbolic sequence” paradigm for multi-task Earth observation.

0 citationsRead paper