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arXiv Machine Learning By Zekai Shi, Meng Zhang, Haokun Zhang, Bo Zhang

Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision

Read the original on arXiv Machine Learning →

The paper introduces SCOPE, a method for reconstructing high‑resolution digital elevation models (DEMs) from coarser‑resolution training pairs. SCOPE learns a continuous terrain representation by predicting a latent coefficient field on a low‑resolution grid and reusing local Fourier residual functions, thereby decoupling coefficient prediction from output‑grid construction. Experiments on diverse land–ocean datasets show that SCOPE outperforms competing methods across six metrics, reduces reconstruction error by about 12 % at three‑times‑unseen scale, and achieves these gains with only a modest increase in computational cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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