# Estimating Canopy Height at Scale

arXiv: https://arxiv.org/abs/2406.01076 · HTML: https://mlsanity.com/paper/2406.01076/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Deep Ensembles: A Loss Landscape Perspective](https://mlsanity.com/paper/1912.02757/) (2019)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Pyramid Attention Network for Semantic Segmentation](https://mlsanity.com/paper/1805.10180/) (2018)
- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging](https://mlsanity.com/paper/2306.16788/) (2023)
- [How I Learned to Stop Worrying and Love Retraining](https://mlsanity.com/paper/2111.00843/) (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)
- [Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights](https://mlsanity.com/paper/2304.11487/) (2023)

## Built on by (most cited first, 16 of 16)

- [Open-Canopy: Towards Very High Resolution Forest Monitoring](https://mlsanity.com/paper/2407.09392/) (2024)
- [AGBD: A Global-scale Biomass Dataset](https://mlsanity.com/paper/2406.04928/) (2024)
- [Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation](https://mlsanity.com/paper/2408.04523/) (2024)
- [Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation](https://mlsanity.com/paper/2501.19328/) (2025)
- [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)
- [Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery](https://mlsanity.com/paper/2506.04970/) (2025)
- [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)
- [Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?](https://mlsanity.com/paper/2501.18527/) (2025)
- [DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications](https://mlsanity.com/paper/2502.17066/) (2025)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [Trees as Gaussians: Large-Scale Individual Tree Mapping](https://mlsanity.com/paper/2508.21437/) (2025)
- [From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps](https://mlsanity.com/paper/2607.24532/) (2026)
- [Multi-Modal, Multi-Temporal Geospatial Foundation Models Improve Global Above-Ground Biomass Estimation](https://mlsanity.com/paper/2608.04792/) (2026)
- [Estimating Individual Tree Height and Species from UAV Imagery](https://mlsanity.com/paper/2603.23669/) (2026)
- [Canopy Tree Height Estimation Using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensing](https://mlsanity.com/paper/2604.06988/) (2026)
- [Counting Trees from Satellite Imagery with Noisy Supervision](https://mlsanity.com/paper/2606.24786/) (2026)

## Abstract (arXiv)

We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE / RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale maps. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring.