AI RESEARCH
SERA-H: Beyond Native Sentinel Spatial Limits for High-Resolution Canopy Height Mapping
arXiv CS.CV
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ArXi:2512.18128v3 Announce Type: replace High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imagery to predict height maps, these approaches often face a trade-off between data accessibility and spatial resolution. To overcome these limitations, we present SERA-H, an end-to-end model combining a super-resolution module (EDSR) and temporal attention encoding.