地球观测回归任务的不确定性量化：建筑物高度、树冠高度和地上生物量估计

中文摘要 AI 辅助

地球观测回归任务如建筑物高度、树冠高度和地上生物量估计在城市规划、森林监测和气候政策等关键应用中起着重要作用，准确性和可靠性至关重要。然而，大多数深度学习模型仅产生确定性预测，无法提供每个像素的可靠性指示。由于陆地表面异质性、目标分布偏斜、传感器噪声和高目标值处的信号饱和，这些回归任务具有固有挑战性，使得不确定性估计对于可靠推理至关重要。我们通过使用长达一年的哨兵 -1 SAR和哨兵 -2 MSI时间序列对偶然不确定性进行建模来解决这一差距，提出了两种互补方法：（i）高斯不确定性，在高斯假设下联合预测均值和标准差；（ii）分位数不确定性，估计第10、50和90分位数以捕获不对称和异方差误差分布。两个模型在10米空间分辨率的三个代表性地球观测回归任务上进行了评估。结果表明，两种方法均匹配或超过确定性基准和现有全球产品，同时提供校准良好、可解释且在操作上有用的置信估计。值得注意的是，两个模型在树冠高度估计方面均优于当前10米的最先进不确定性感知模型。我们的实现将在：此https URL上提供。

英文摘要

Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical. Yet most deep learning models yield only deterministic predictions, providing no indication of per-pixel reliability. These regression tasks are inherently challenging due to heterogeneous land surfaces, skewed target distributions, sensor noise, and signal saturation at high target values, making uncertainty (UC) estimation essential for reliable inference. We address this gap by modeling aleatoric uncertainty using year-long Sentinel-1 SAR and Sentinel-2 MSI time series, proposing two complementary approaches: (i) Gaussian UC, which jointly predicts mean and standard deviation under a Gaussian assumption, and (ii) Quantile UC, which estimates the 10th, 50th, and 90th quantiles to capture asymmetric and heteroscedastic error distributions. Both models are evaluated on three representative EO regression tasks at 10 m spatial resolution. Results show that both approaches match or surpass deterministic benchmarks and existing global products, while delivering well-calibrated, interpretable, and operationally useful confidence estimates. Notably, both models outperform the current 10 m state-of-the-art uncertainty-aware model for canopy height estimation. Our implementation will be available at: https://github.com/RituYadav92/EO-Regression-Uncertainty-Estimation

发表机构

• Division of Geoinformatics, KTH Royal Institute of Technology（地理信息学部，瑞典皇家理工学院）

机构由 AI 辅助整理，请以论文原文为准。