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M. Brovelli

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Open access Aug 2026

Improving Sentinel-5P Imagery Usability Through Machine Learning Gap-Filling

This study investigates missing data patterns and develops a robust gap-filling framework for the Po Valley (Northern Italy) over 2019–2023, a region characterized by complex basin topography and frequent winter inversions that exacerbate both pollution accumulation and data loss.

Zhanbing Wu, V. Yordanov, M. Brovelli · 0 citations
#machine learning Preprint Sep 2026

From Foundation Embeddings to Cropland Maps: Label Efficiency, Temporal Transferability and Independent Human Validation

The results support frozen geospatial embeddings as a low-compute candidate for regional cropland mapping, subject to the limits of a single-state study, a 30 m-derived training reference, and a one-block human validation.

Mohammad Ammar Mughees, Giovanni Montefoschi, Zhong-Xin Chen et al. · 0 citations

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