# A high-resolution canopy height model of the Earth

arXiv: https://arxiv.org/abs/2204.08322 · HTML: https://mlsanity.com/paper/2204.08322/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [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)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning](https://mlsanity.com/paper/2002.06470/) (2020)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Improving Image Classification with Location Context](https://mlsanity.com/paper/1505.03873/) (2015)
- [Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles](https://mlsanity.com/paper/2103.03975/) (2021)
- [Country-wide high-resolution vegetation height mapping with Sentinel-2](https://mlsanity.com/paper/1904.13270/) (2019)
- [Mapping oil palm density at country scale: An active learning approach](https://mlsanity.com/paper/2105.11207/) (2021)
- [Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles](https://mlsanity.com/paper/2111.13154/) (2021)
- [High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR](https://mlsanity.com/paper/2107.07431/) (2021)

## Built on by (most cited first, 30 of 91)

- [MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning](https://mlsanity.com/paper/2405.02771/) (2024)
- [Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks](https://mlsanity.com/paper/2310.06743/) (2023)
- [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)
- [TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis](https://mlsanity.com/paper/2506.20380/) (2025)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward](https://mlsanity.com/paper/2305.08413/) (2023)
- [RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings](https://mlsanity.com/paper/2502.19781/) (2025)
- [Estimating Canopy Height at Scale](https://mlsanity.com/paper/2406.01076/) (2024)
- [Open-Canopy: Towards Very High Resolution Forest Monitoring](https://mlsanity.com/paper/2407.09392/) (2024)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [OAM-TCD: A globally diverse dataset of high-resolution tree cover maps](https://mlsanity.com/paper/2407.11743/) (2024)
- [TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data](https://mlsanity.com/paper/2504.11172/) (2025)
- [AGBD: A Global-scale Biomass Dataset](https://mlsanity.com/paper/2406.04928/) (2024)
- [Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors](https://mlsanity.com/paper/2407.10330/) (2024)
- [WildSAT: Learning Satellite Image Representations from Wildlife Observations](https://mlsanity.com/paper/2412.14428/) (2024)
- [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)
- [Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation](https://mlsanity.com/paper/2408.04523/) (2024)
- [Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model](https://mlsanity.com/paper/2306.01936/) (2023)
- [Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation](https://mlsanity.com/paper/2501.19328/) (2025)
- [Lossy Neural Compression for Geospatial Analytics: A Review](https://mlsanity.com/paper/2503.01505/) (2025)
- [Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction](https://mlsanity.com/paper/2401.06421/) (2024)
- [REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation](https://mlsanity.com/paper/2412.16583/) (2024)
- [U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2305.13277/) (2023)
- [Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights](https://mlsanity.com/paper/2304.11487/) (2023)
- [MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training](https://mlsanity.com/paper/2602.06285/) (2026)
- [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)
- [High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model](https://mlsanity.com/paper/2501.10600/) (2025)
- [SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology](https://mlsanity.com/paper/2504.18256/) (2025)
- [SelvaBox: A high-resolution dataset for tropical tree crown detection](https://mlsanity.com/paper/2507.00170/) (2025)
- [Active Measurement: Efficient Estimation at Scale](https://mlsanity.com/paper/2507.01372/) (2025)

## Abstract (arXiv)

The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestrial ecosystems, mitigate climate change, and prevent biodiversity loss. Here, we present the first global, wall-to-wall canopy height map at 10 m ground sampling distance for the year 2020. No single data source meets these requirements: dedicated space missions like GEDI deliver sparse height data, with unprecedented coverage, whereas optical satellite images like Sentinel-2 offer dense observations globally, but cannot directly measure vertical structures. By fusing GEDI with Sentinel-2, we have developed a probabilistic deep learning model to retrieve canopy height from Sentinel-2 images anywhere on Earth, and to quantify the uncertainty in these estimates. The presented approach reduces the saturation effect commonly encountered when estimating canopy height from satellite images, allowing to resolve tall canopies with likely high carbon stocks. According to our map, only 5% of the global landmass is covered by trees taller than 30 m. Such data play an important role for conservation, e.g., we find that only 34% of these tall canopies are located within protected areas. Our model enables consistent, uncertainty-informed worldwide mapping and supports an ongoing monitoring to detect change and inform decision making. The approach can serve ongoing efforts in forest conservation, and has the potential to foster advances in climate, carbon, and biodiversity modelling.