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Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation

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

Record details

Published: 13 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Environment
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (13 July 2026), “Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation,” evidence record 3061, https://ethics.ai/record/3061 (originally published by arXiv cs.AI).

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