Abstract
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 (UTAE). Trained under the supervision of high-density LiDAR data (ALS), our model generates 2.5 m resolution height maps from freely available Sentinel-1 and Sentinel-2 (10 m) time series data. Evaluated on an open-source benchmark dataset in France, SERA-H, with a MAE of 2.6 m and a coefficient of determination of 0.82, not only outperforms standard Sentinel-1/2 baselines but also achieves performance comparable to or better than methods relying on commercial very high-resolution imagery (SPOT-6/7, PlanetScope, Maxar). These results demonstrate that combining high-resolution supervision with the spatiotemporal information embedded in time series enables the reconstruction of details beyond the input sensors' native resolution. SERA-H opens the possibility of freely mapping forests with high revisit frequency, achieving accuracy comparable to that of costly commercial imagery.
Abstract (translated)
高分辨率的树冠高度测绘对于森林管理和生物多样性监测至关重要。尽管最近的研究已导致使用卫星图像进行深度学习方法以预测高度图谱的发展，但这些方法通常在数据可访问性和空间分辨率之间存在权衡。为了解决这些问题，我们提出了SERA-H模型，这是一种结合了超分辨率模块（EDSR）和时间注意力编码（UTAE）的端到端模型。我们的模型在高密度激光雷达数据（ALS）的监督下训练，并能从免费提供的Sentinel-1和Sentinel-2（10米）时序数据生成2.5米分辨率的高度图。 在一个法国公开源代码基准数据集上进行评估，SERA-H模型具有2.6米的均方根误差(MAE) 和 0.82 的决定系数，在性能上不仅超越了标准Sentinel-1/2基线方法，也达到了与依赖于商业高分辨率图像（SPOT-6/7、PlanetScope和Maxar）的方法相当或更好的水平。这些结果表明，结合高分辨率监督以及时序数据中嵌入的时空信息可以重建超出输入传感器原生分辨率的细节。 SERA-H模型开启了以低成本实现高频重访的自由森林测绘的可能性，并且其准确性可与昂贵的商业图像相媲美。
URL
https://arxiv.org/abs/2512.18128
