# Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles

arXiv: https://arxiv.org/abs/2103.03975 · HTML: https://mlsanity.com/paper/2103.03975/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Bayesian Deep Learning and a Probabilistic Perspective of Generalization](https://mlsanity.com/paper/2002.08791/) (2020)
- [Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision](https://mlsanity.com/paper/1906.01620/) (2019)
- [Lightweight Probabilistic Deep Networks](https://mlsanity.com/paper/1805.11327/) (2018)
- [The Case for Bayesian Deep Learning](https://mlsanity.com/paper/2001.10995/) (2020)
- [Country-wide high-resolution vegetation height mapping with Sentinel-2](https://mlsanity.com/paper/1904.13270/) (2019)

## Built on by (most cited first, 26 of 26)

- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar](https://mlsanity.com/paper/2304.07213/) (2023)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass](https://mlsanity.com/paper/2112.11335/) (2021)
- [High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach](https://mlsanity.com/paper/2212.10265/) (2022)
- [Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles](https://mlsanity.com/paper/2111.13154/) (2021)
- [Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans](https://mlsanity.com/paper/2204.11620/) (2022)
- [Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas](https://mlsanity.com/paper/2411.14354/) (2024)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation](https://mlsanity.com/paper/2406.19888/) (2024)
- [Scaling Up Forest Vision with Synthetic Data](https://mlsanity.com/paper/2509.11201/) (2025)
- [Process-Guided Concept Bottleneck Model](https://mlsanity.com/paper/2601.10562/) (2026)
- [Satellite-based high-resolution maps of cocoa planted area for Cote d'Ivoire and Ghana](https://mlsanity.com/paper/2206.06119/) (2022)
- [MapInWild: A Remote Sensing Dataset to Address the Question What Makes Nature Wild](https://mlsanity.com/paper/2212.02265/) (2022)
- [Uncertainty Quantification for Image-based Traffic Prediction across Cities](https://mlsanity.com/paper/2308.06129/) (2023)
- [Uncertainty evaluation of segmentation models for Earth observation](https://mlsanity.com/paper/2510.19586/) (2025)
- [Geo3DVQA: Evaluating Vision-Language Models for 3D Geospatial Reasoning from Aerial Imagery](https://mlsanity.com/paper/2512.07276/) (2025)
- [SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation](https://mlsanity.com/paper/2212.11538/) (2022)
- [Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data](https://mlsanity.com/paper/2311.11777/) (2023)
- [Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation](https://mlsanity.com/paper/2504.19589/) (2025)
- [Deep Imbalanced Multi-Target Regression: 3D Point Cloud Voxel Content Estimation in Simulated Forests](https://mlsanity.com/paper/2511.12740/) (2025)
- [SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation](https://mlsanity.com/paper/2511.17600/) (2025)
- [Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation](https://mlsanity.com/paper/2602.06503/) (2026)
- [Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation](https://mlsanity.com/paper/2607.11412/) (2026)
- [Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery](https://mlsanity.com/paper/2608.06406/) (2026)

## Abstract (arXiv)

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks(CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias.