THREASURE-Net

Super-Resolved Canopy Height Mapping from Sentinel-2 Time Series Using LiDAR HD Reference Data across Metropolitan France

Fine-scale forest monitoring is essential for understanding canopy structure and its dynamics, which are key indicators of carbon stocks, biodiversity, and forest health. Deep learning is particularly effective for this task as it integrates spectral, temporal, and spatial signals that jointly reflect the canopy structure. To address this need, we introduce THREASURE-Net, a novel end-to-end framework for Tree Height Regression And Super-Resolution. The model is trained on Sentinel-2 time series using reference height metrics derived from LiDAR HD data at multiple spatial resolutions over Metropolitan France to produce annual height maps. We evaluate three model variants, producing tree-height predictions at 2.5 m, 5 m, and 10 m resolution. THREASURE-Net does not rely on any pretrained model nor on reference very high resolution optical imagery to train its super-resolution module; instead, it learns solely from LiDAR-derived height information. Our approach outperforms existing state-of-the-art methods based on Sentinel data and is competitive with methods based on very high resolution imagery. It can be deployed to generate high-precision annual canopy-height maps, achieving mean absolute errors of 2.62 m, 2.72 m, and 2.88 m at 2.5 m, 5 m, and 10 m resolution, respectively. These results highlight the potential of THREASURE-Net for scalable, cost-effective structural monitoring of temperate forests using only freely available satellite data.

Installation

This project uses Pixi to manage environments and dependencies.

Requirements

• Git
• Pixi

Clone the repository

git clone https://github.com/Global-Earth-Observation/threasure-net.git
cd threasure-net

Install dependencies

Install all dependencies defined in pyproject.toml:

pixi install -e gpu
pixi shell -e gpu
conda activate ./.pixi/envs/gpu

Model resolution

The model can be run at three different spatial resolutions:

• 2.5 m
• 5 m
• 10 m

Each resolution uses a specific configuration file.

To train model at each resolution, you must run respectively:

python src/bin/train.py experiment=sits_rdb_pe_tq_2_5m
python src/bin/train.py experiment=sits_rdb_pe_tq_5m
python src/bin/train.py experiment=sits_rdb_pe_tq_10m

Input data

The input data must be organized as follows.

Overview

• Sentinel-2 data and acquisition angles (optional) are used as input data.
• LiDAR HD 95th height percentiles are used as reference data.
• Reference LiDAR masks.

Reference LiDAR masks are used to restrict predictions to tree heights (i.e. permanent vegetation). Since automatically pre-processed data were used, some seasonal vegetation (e.g. crops) was preclassified as permanent vegetation; crop masks are therefore applied to remove these areas.

LiDAR HD heights can be:

• provided directly at the desired spatial resolution, or
• provided at 1 m spatial resolution, in which case the model will handle the resampling.

The spatial resolution of the LiDAR HD heights must be specified in:

hydra/datamodule/sits.yaml

single_tile_config:
  target_resolution: <value>

This resolution can differ from the desired output resolution.

The paths to data should be provided in

Sentinel-2 data

Sentinel-2 time series are organized by S2 tile and patch. A metadata parquet file is associated with each tile. For example:

30TXT/
├── 30TXT.parquet
├── 0436_6734/
├── ...
├── 0440_6740/
│   ├── SENTINEL2B_20240706_30TXT_0440_6740.tif
│   └── SENTINEL2A_20240919_30TXT_0440_6740.tif
└── ...

The 30TXT.parquet file contains patch-level metadata associated with the Sentinel-2 acquisitions:

index	lidarhd_id	context	lidar_date	lidar_name	s2_date	sensor	tile_name	image_name	mask_name	masked_pixels	valid_pixels	ratio_cloud	ratio_border
0	0437_6737	training	2024-01-27	percentiles_0437_6737_2024-01-27_0.95.tif	2024-09-12	SENTINEL2A	30TXT	SENTINEL2A_20240912_30TXT_0437_6737.tif	MASK_SENTINEL2A_20240912_30TXT_0437_6737.tif	23	100	7	0
1	0437_6742	training	2024-01-27	percentiles_0437_6742_2024-01-27_0.95.tif	2024-09-12	SENTINEL2A	30TXT	SENTINEL2A_20240912_30TXT_0437_6742.tif	MASK_SENTINEL2A_20240912_30TXT_0437_6742.tif	100	100	68	0
3	0437_6746	training	2024-01-27	percentiles_0437_6746_2024-01-27_0.95.tif	2024-09-12	SENTINEL2A	30TXT	SENTINEL2A_20240912_30TXT_0437_6746.tif	MASK_SENTINEL2A_20240912_30TXT_0437_6746.tif	100	100	89	0
7	0436_6735	training	2024-01-27	percentiles_0436_6735_2024-01-27_0.95.tif	2024-09-12	SENTINEL2A	30TXT	SENTINEL2A_20240912_30TXT_0436_6735.tif	MASK_SENTINEL2A_20240912_30TXT_0436_6735.tif	99	100	0	0
8	0441_6747	training	2024-01-27	percentiles_0441_6747_2024-01-27_0.95.tif	2024-09-12	SENTINEL2A	30TXT	SENTINEL2A_20240912_30TXT_0441_6747.tif	MASK_SENTINEL2A_20240912_30TXT_0441_6747.tif	83	100	20	0

ratio_border indicates the proportion of NaN pixels due to partial Sentinel-2 tile coverage from orbit overlaps.

LiDAR HD data

LiDAR HD patches follow the same S2 tile and patch structure and organized by resolution:

30TXT/
└── res_1/
    ├── pH95_0440_6740_2024-01-27.tif
    └── ...

Each file contains the 95th height percentile for the corresponding patch.

Acquisition and solar angles (optional)

Sentinel-2 acquisition and solar angles are stored in a CSV file, organized by S2 tile:

30TXT.csv

Angles are optional and provided as a vector per patch, as they are available at coarse spatial resolution (≈5 km) and only their ordering is required.

CSV format

The CSV file must contain the following columns:

Column name	Description
patch_id	Patch identifier (top left corner coordinates)
image_name	Sentinel-2 image file name
x	X coordinate of the patch center
y	Y coordinate of the patch center
sun_zenith	Solar zenith angle (degrees)
sun_azimuth	Solar azimuth angle (degrees)
view_zenith	Sensor viewing zenith angle (degrees)
view_azimuth	Sensor viewing azimuth angle (degrees)

Example:

patch_id	image_name	x	y	sun_zenith	sun_azimuth	view_zenith	view_azimuth
0440_6740	SENTINEL2B_20240706_30TXT_0440_6740.tif	6739500	440500	27.7	149.9	2.8	134.8
0437_6752	SENTINEL2B_20240706_30TXT_0437_6752.tif	6751500	437500	27.8	149.9	3.3	129.8
0447_6749	SENTINEL2B_20240706_30TXT_0447_6749.tif	6748500	447500	27.7	150.1	2.6	138.2
0436_6747	SENTINEL2B_20240706_30TXT_0436_6747.tif	6746500	436500	27.7	149.8	3.3	130.5