# Country-wide high-resolution vegetation height mapping with Sentinel-2

arXiv: https://arxiv.org/abs/1904.13270 · HTML: https://mlsanity.com/paper/1904.13270/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 22 of 22)

- [A high-resolution canopy height model of the Earth](https://mlsanity.com/paper/2204.08322/) (2022)
- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles](https://mlsanity.com/paper/2103.03975/) (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)
- [Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model](https://mlsanity.com/paper/2306.01936/) (2023)
- [Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles](https://mlsanity.com/paper/2111.13154/) (2021)
- [TIML: Task-Informed Meta-Learning for Agriculture](https://mlsanity.com/paper/2202.02124/) (2022)
- [Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights](https://mlsanity.com/paper/2304.11487/) (2023)
- [High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR](https://mlsanity.com/paper/2107.07431/) (2021)
- [Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data](https://mlsanity.com/paper/2111.02061/) (2021)
- [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)
- [Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs](https://mlsanity.com/paper/2106.03774/) (2021)
- [Satellite-based high-resolution maps of cocoa planted area for Cote d'Ivoire and Ghana](https://mlsanity.com/paper/2206.06119/) (2022)
- [A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data](https://mlsanity.com/paper/2212.00246/) (2022)
- [Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images](https://mlsanity.com/paper/2308.05005/) (2023)
- [First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia](https://mlsanity.com/paper/2404.14661/) (2024)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain](https://mlsanity.com/paper/2309.01797/) (2023)
- [Decadal sink-source shifts of forest aboveground carbon since 1988](https://mlsanity.com/paper/2506.11879/) (2025)
- [Scalable deep fusion of spaceborne lidar and synthetic aperture radar for global forest structural complexity mapping](https://mlsanity.com/paper/2510.06299/) (2025)
- [FORMSpoT: Revealing Fine-Scale Forest Disturbances from Nation-Wide 1.5 m Forest Canopy Height Time Series](https://mlsanity.com/paper/2512.17021/) (2025)
- [Estimating Individual Tree Height and Species from UAV Imagery](https://mlsanity.com/paper/2603.23669/) (2026)

## Abstract (arXiv)

Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and 5.6 m, respectively), and correctly estimate vegetation heights up to >50 m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data (i.e., 2000 km2 in Gabon and ≈5800 km2 in Switzerland), high-resolution vegetation height maps with 10 m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery.