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These findings prove that physically accurate synthetic data provides a more effective supervisory signal for learning geometric features than complex real-world observations, establishing a new paradigm for Sim-to-Real transfer in Earth Observation.
ELMZip formulates the fitting process as a convex least-squares problem using random-feature single-layer networks, thereby eliminating the need for computationally expensive backpropagation and achieves significant compression efficiency while maintaining high reconstruction fidelity.
This work proposes a "Summarize First, Download Later" paradigm that exploits recent advances in onboard edge computing and Vision-Language Models (VLMs) and substantially reduces bandwidth consumption while accelerating time-to-insight for time-sensitive missions.
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