🔥 UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation

💡 The paper introduces UniverSat, a new approach to applying Vision Transformers to Earth Observation data. The problem with current Vision Transformers is that they rely on rigid patch projectors, which makes it difficult to transfer them to Earth Observation tasks where the input data can vary widely in terms of modality, scale, and resolution. To address this issue, the authors propose a Universal Patch Encoder that can map patches from arbitrary spatial, spectral, and temporal resolutions, and from both optical and non-optical sensors, into a shared embedding space using a shared set of weights. This allows a single model to be trained on heterogeneous multimodal data using self-supervision, resulting in robust and sensor-agnostic spatial features. The authors validate their approach by achieving strong results on classification and segmentation tasks using standard Earth Observation benchmarks. The key contribution of UniverSat is its ability to enable resolution- and modality-agnostic spatial feature extraction, making it a versatile and effective tool for Earth Observation tasks. The authors make their code and models available for further research and development.

📅 Published on Jun 22

🔗 Links:
• GitHub: https://github.com/huggingface
• Project Page: https://huggingface.co/papers?q=patch%20projectors
• arXiv: https://arxiv.org/abs/2606.23503
• PDF: https://arxiv.org/pdf/2606.23503

🤖 Models citing this paper:
• https://huggingface.co/g-astruc/UniverSat

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📢 By: https://xn--r1a.website/PaperNexus

#EarthObservation #VisionTransformers #MultimodalLearning #RemoteSensing #GeospatialAnalysis