# Open-Canopy: A Country-Scale Benchmark for Canopy Height Estimation at Very
High Resolution

Fajwel Fogel4 &Yohann Perron3,5 &Nikola Besic2 &Laurent Saint-André7 &Agnès
Pellissier-Tanon1 &Martin Schwartz1 &Thomas Boudras1 &Ibrahim Fayad1,6
&Alexandre d’Aspremont4,6
1 LSCE/IPSL, CEA-CNRS-UVSQ
2 LIF, IGN, ENSG &Loic Landrieu3
3 LIGM, Ecole des Ponts, CNRS, UGE
4 CNRS & École Normale Supérieure
7 INRAE, BEF &Philippe Ciais1
5 EFEO
6 Kayrros

###### Abstract

Estimating canopy height and canopy height change at meter resolution from
satellite imagery has numerous applications, such as monitoring forest health,
logging activities, wood resources, and carbon stocks. However, many existing
forest datasets are based on commercial or closed data sources, restricting
the reproducibility and evaluation of new approaches. To address this gap, we
introduce Open-Canopy, the first open-access and country-scale benchmark for
very high resolution (1.5 m) canopy height estimation. Covering more than
87,000 km2 across France, Open-Canopy combines SPOT satellite imagery with
high resolution aerial LiDAR data. We also propose Open-Canopy-$\Delta$, the
first benchmark for canopy height change detection between two images taken at
different years, a particularly challenging task even for recent models. To
establish a robust foundation for these benchmarks, we evaluate a
comprehensive list of state-of-the-art computer vision models for canopy
height estimation. The dataset and associated codes can be accessed at
https://github.com/fajwel/Open-Canopy.

##  1 Introduction

National and regional forestry agencies have traditionally developed their
management strategies around a 10 to 20-year planning cycle [1, 2]. However,
the accelerating pace of climate change requires a shift towards more
responsive forest management practices [3, 4, 5]. This implies monitoring the
evolution of forest resources at a national level with high spatial resolution
and, ideally, on a yearly basis. Although Aerial Laser Scanning (ALS) provides
rich information about canopy height [6], its high cost and logistic
requirements make annual campaigns unrealistic. However, the acquired ALS data
can be used to train machine learning models to estimate canopy height from a
single Very High Resolution (VHR) satellite image, offering a cost-effective
alternative for annual monitoring. To support the evaluation and development
of these tools, we introduce Open-Canopy, an open-access dataset specifically
designed for VHR canopy height estimation. In addition, we propose the first
benchmark for the task of estimating the canopy height change between two
images.

|  |  |  |  0m10m20m30m40m
---|---|---|---|---
|  |  |

whiteNwhite50m (a) VHR images† (1.5m)

(b) ALS height map† (1.5m)

(c) Prediction † (ours, 1.5m) MAE: 3.32m

(d) Tolan†[7] (1.2m) MAE: 4.34m

(e) Liu† [8] (3m) MAE: 6.93m

(f) Schwartz† [9] (10m) MAE: 6.39m

(g) Lang† [10] (10m) MAE: 15.87m

(h) Potapov† [11] (30m) MAE: 8.75m

Figure 1: Canopy Height Estimation. We represent a VHR image from the Open-
Canopy test set (1(a)), alongside its corresponding ALS-derived canopy height
(1(b)). We also include the height map predicted by a PVPTv2 [12] model
trained on the Open-Canopy train set (1(c)), compared against other canopy
height products (1(d)-1(h)). We provide the spatial resolution and the Mean
Absolute Error (MAE) for the pixels within the image’s vegetation mask (see
Section 3.1). $\dagger$ indicates that the data or the model used to generate
this maps is not open-access.

High-Resolution Canopy Height Estimation. The spatial and temporal resolution
of canopy height estimation is critical to several of its key applications. As
seen in Figure 1, higher spatial resolution enables the detection of smaller
vegetation, such as shrubs and hedges [13], which play a vital role in
ecosystem health [14]. Furthermore, observations at scales close to individual
trees lead to insights into biodiversity [15] and a more accurate proxy for
biomass estimation [16].

The benefit of estimating canopy height at repeated intervals from multiple
satellite images allows for monitoring forest growth or decline over time.
Tracking these changes is crucial for understanding forest health dynamics
[17, 18], detecting tree mortality due to disturbances [19], and removals from
both harvest [20] and illegal logging activities [21, 22]. In addition, it
supports the creation of maps of biomass carbon stock essential for national
inventories [23, 24, 25]. High-resolution growth monitoring enables the
detection of specific forest management practices beyond the more visible
disturbances, such as clear-cutting. Selective logging, for example, can be
identified [26, 27], thereby improving forest management and conservation
efforts.

The Open-Canopy Benchmark. Recent advancements in deep learning have enabled
the prediction of canopy heights from satellite images. The spaceborne LiDAR
GEDI [28] has been particularly transformative, allowing models to generate
global canopy height maps [29], although typically at lower resolutions of
$10$ to $30$m per pixel [10, 11, 9]. Additionally, high-resolution height maps
derived from ALS data, when paired with VHR imagery, can be used to train
canopy height prediction models [7, 30, 8]. However, with the notable
exception of Lang et al. [10], these research efforts do not make their
datasets readily accessible. The locations of training and testing sets are
often undisclosed, substantial pre-processing of the data is required, and in
some cases, the data depend on costly commercial imagery. Leveraging recent
open-source initiatives by the French government [31, 32], we propose Open-
Canopy, an open-access and country-scale canopy height benchmark with VHR
satellite imagery (1.5m resolution) and ALS-based supervision, covering 87,400
km2 in France. We provide ready-to-use data, official train and test splits,
and all pre-processing scripts.

We also propose Open-Canopy-$\Delta$, the first benchmark for forest height
change detection. The task consists in detecting areas with significant
reduction in canopy height between two consecutive VHR satellite images taken
in 2022 and 2023. The ground truth was derived from two ALS acquisitions and
covers a $166$km2 area of France.

Canopy Height Estimation Models. While the majority of existing studies in
canopy height estimation have focused on UNet-type architectures [33], we
propose to evaluate various modern architectures for dense predictions,
including Vision Transformers (ViT)-based architectures [34] and several
vision foundation models.

Our contributions are as follows:

  * •

Open-Canopy. We propose a novel open-access benchmark for canopy height
estimation from VHR images with dense ALS annotations.

  * •

Extensive Evaluations. We evaluate several recent computer vision
architectures.

  * •

Open-Canopy-$\Delta$. We propose the first benchmark for detecting significant
canopy height changes between two images.

##  2 Related Work

This section details existing datasets and methods for the problem of canopy
height estimation, grouped by annotation type: GEDI or ALS. See Table 1 for an
exhaustive comparison of Open-Canopy with these datasets.

Table 1: Existing Forestry Datasets. We detail existing datasets used to train and evaluate canopy height estimation models. We report information about the satellite images used as input, the canopy height maps used to train and evaluate models, and the number of ground truth elevation data points (samples). We denote open-access and directly downloadable data sources with ✔, while data under commercial license are denoted with . Data sources that necessitate special access are denoted with , complex preprocessing with , and for which the location of the train/test sets are missing with . S1/S2 refer to Sentinel 1 [35] and 2 [36] and NAIP to the National Agriculture Imagery Program [37]. Dataset | code |  | extent |  | observations |  | annotations
---|---|---|---|---|---|---|---
OA |  | scope | surface |  | nature | res. | OA |  | nature | res. | samples | OA
|  $\times 10^{3}$ km2 |  | in m |  | in m |  $\times 10^{6}$ pts
Schwartz [9] | ✗ |  | France | 588 |  | S1/S2 | 10 |  |  | GEDI | 25 | 90 |
Lang [10] | ✔ |  | Global | 13,500 |  | S2 | 10 | ✔ |  | GEDI | 25 | 600 | ✔
Potapov [11] | ✗ |  | Global | 150,000 |  | Landsat | 30 |  |  | GEDI | 25 | 372 |
Tolan [7] | ✗ |  | US | 5.8 |  | MAXAR | 1.2 |  |  | ALS | 1 | 5800 | ✔
Wagner [30] | ✗ |  | US | 3.8 |  | NAIP | 0.6 |  |  | ALS | 1 | 3784 |
Liu [8] | ✗ |  | Europe | 700 |  | Planet | 3 |  |  | ALS | 3 | 77,777 |
Open-Canopy | ✔ |  | France | 87 |  | SPOT 6-7 | 1.5 | ✔ |  | ALS | 1.5 | 38,876 | ✔

GEDI-Based Datasets. The Global Ecosystem Dynamics Investigation (GEDI)
mission consists of a LiDAR mounted on the ISS and provides global canopy
height measurements with a footprint diameter of 25m [28]. GEDI captures a set
of spatially discrete full waveform echoes along paths approximately 4 km
wide, offering sparse yet rich information about the vegetation structure.
Models trained with GEDI data use it as a sparse and coarse supervisory signal
to predict canopy heights from medium to high resolution imagery such as
Landsat images at 30m resolution (Potapov [11]) or Sentinel-2 at 10 m
resolution (Schwartz [9] and Lang [10]). However, GEDI’s full waveform LiDAR
has registration errors of typically 10m [38].

ALS-Based Datasets. Aerial Laser Scanning (ALS) uses low-flying aircraft
equipped with LiDAR to create dense 3D point clouds of the Earth’s surface.
These systems typically capture data at resolutions ranging from 10 to 60
points per square meter. This data is then rasterized along a high resolution
grid, and used to estimate canopy height by subtracting the lowest quantile
height (ground surface) from the height of the highest quantile (top of
canopy). This allows the computation of “true” height maps at scales such as
$1.2$m for Tolan [7] and Wagner [30], $3$ m for Liu [8]. Open-Canopy use ALS
data from the LiDAR-HD [31] program rasterized at $1.5$ m. These maps are then
used as ground truth to train models that predict canopy height from
corresponding VHR images.

Canopy Height Estimation Models. Most canopy height prediction models employ
fully supervised UNets [33] for their ease of use. The recent work by Tolan
_et al_. uses a Vision Transformer (ViT) [34] pretrained in a self-supervised
fashion [39] on 18 million images without ALS height data. In this paper, we
benchmark a variety of modern deep learning architectures for dense prediction
of VHR canopy height from SPOT images, including Unet [33], Vision
Transformers (ViT) [34], and their hierarchical variants [40, 12, 41]. We also
explore how their pretraining impact their ability to adapt from vision-
related problems to the completely different task of canopy height estimation.

Canopy Height Change Estimation. As forests experience rapid losses [42, 43],
better understanding and monitoring of forest dynamics is critical [44]. While
existing studies have explored the long-term evolution of forests [45, 46,
47], they focus on environmental or phenological variables and low resolution
(500m) imagery [48]. Forest change detection models generally operate at
medium or high resolutions (10-30m) [49, 50]. Few attempts have been made to
measure forest volume [51] or volume changes [52], using UAVs over small areas
and without providing open-access data or code. In contrast, we introduce the
first open-access, large-scale VHR benchmark for canopy height change
detection with LiDAR-derived ground truth.

Data Access Policies. The six studies in Table 1 all provide open-access
predicted canopy height maps and often their trained models. However, only the
work of Lang _et al_. provides its code and direct download links for their
processed datasets. In contrast, the datasets used by Tolan _et al_., Wagner
_et al_., and Liu _et al_. involve commercial satellite imagery or data that
requires special access and cannot be redistributed. Although GEDI, Sentinel,
and Landsat data are open-access, their preprocessing necessitates substantial
expertise. Except for the studies of Tolan _et al_. and Lang _et al_., these
works also do not specify their training and testing splits, complicating
their evaluation on external datasets due to potential overlap. Like the study
of Lang _et al_., our data, code, splits, and models are freely available.
This transparency is crucial for advancing canopy height estimation as a
mainstream application of vision models.

##  3 The Open-Canopy Benchmark: Estimating Canopy Height

We introduce Open-Canopy, an open-access country-scale benchmark for
estimating canopy height at very high resolution. We first present our dataset
(Section 3.1), then the models evaluated (Section 3.2). Finally, we present
the results (Section 3.2) and limitations (Section 3.4) of the benchmark.

(a) Splits |  whiteNwhite250m (b) VHR satellite image (c) ALS-based height map
---|---

Figure 2: Open-Canopy. Our training, validation, and test sets cover most of
the French territory and use a $1$km buffer (2(a)). For each tile, we provide
VHR images at a 1.5 m resolution (2(b)) and associated LiDAR-derived canopy
height maps (2(c)) .

###  3.1 Dataset Characteristics

We present here the main characteristics of the dataset of the Open-Canopy
benchmark. We report a detailed description of the dataset construction in the
supplementary.

Why Just France? France offers a valuable test bed for canopy height
estimation thanks to recent national open-source initiatives providing access
to two critical data sources under the open EtaLab2.0 licence [53]: (i)
DINAMIS [32], which supplies SPOT 6-7 VHR satellite imagery covering all of
French territory at a 1.5 m resolution, and (ii) the LiDAR-HD project [31],
offering airborne 3D point clouds with a density above $10$ points per square
meter. This combination of open-access VHR and ALS allows us to build not only
a very high resolution canopy height map with a method that can be applied to
the whole country, but it also makes it possible to produce maps of height
changes, giving insights on recent tree removals from harvest and natural
mortality.

The French metropolitan territory exhibits a wide range of climates—12 of the
18 Köppen-Geiger climate types found in continental Europe [54], including
temperate, Mediterranean, and alpine environments. The French forest inventory
lists 190 distinct tree species [55]. While models trained on the Open-Canopy
dataset may not generalize globally, their performance within central Europe
is likely to be robust given this environmental diversity.

Extent and Splits. We selected 87,383 tiles, each measuring $1\times 1$ km2,
and spanning a total area of 87,383 km2 across France. We divided the dataset
into training (66,339 km2 ), validation (7,369 km2), and test sets (13,675
km2). We added a 1 km buffer between the test split and other splits to avoid
data contamination (8,046 km2). Test tiles consist in squares of $7\times 7$
km2 spread across all bioclimatic regions of France [56], with the exception
of the larger test area of Chantilly forest, which we use for both height and
change evaluation.

VHR Satellite Images. As illustrated in Figure 32(b), we use orthorectified
SPOT 6-7 images [57, 58] from Dinamis [32] with four spectral bands: red,
green, blue, and near-infrared at a resolution of $6$m, and a panspectral band
at a resolution of $1.5m$. We apply pansharpening with the weighted Brovey
algorithm [59] to upsample all four spectral bands to a resolution of $1.5$m.
We select images from the same year as the corresponding ALS acquisition
campaign, in 2021, 2022 and 2023.

ALS-Based Canopy Height. As depicted in Figure 32(c), we use open-access ALS
data from the LiDAR-HD acquisition campaigns [31] between 2021 and 2023, which
provides a minimum density of $10$ points per m2. The canopy height maps are
calculated at the same resolution as the VHR images by taking the maximum
difference between the height of each point height and the one of the nearest
point classified as ground within its pixel, interpolating values in areas
without data.

Vegetation Mask. As illustrated in Figure 3, we take the union of the ALS-
derived mask indicating vegetation over $1.5$m in height, with the official
forest plots outlines, both provided by IGN [31, 60]. The resulting vegetation
mask covers trees and shrubs within forest plots as well as outside, such as
hedges and urban trees. In contrast, canopy height estimation methods often
only report evaluation of their prediction within forest outlines [9].

|  |  |
---|---|---|---

(a) ALS Vegetation Mask

(b) Forest Outline

(c) Combined Mask

whiteNwhite250m (d) VHR image

Figure 3: Vegetation Mask. We combine an ALS-derived vegetation mask (3(a))
with official forest outlines (3(b)) to build a pixel-precise mask (3(c))
covering a wide range of vegetation types, as seen in the VHR image (3(d)).

###  3.2 Evaluated Models

We evaluate different state-of-the-art computer vision approaches for canopy
height estimation from a single VHR satellite image with 4 spectral bands. We
list below the selected models and how we adapted them to our task.

Selected Models. Given the ubiquity of convolutional models for canopy height
estimation, we evaluate the UNet [33] and DeepLabv3 [61] architectures. We
select Vision Transformers (ViT) and their convolutional-hybrid variant (HViT)
[34], as they recently became standard in computer vision. We also explore
hierarchical ViT architectures such as SWIN [40], PCPVT [41], and PVTv2 [12].

To assess the impact of pretraining, we include models pretrained on ImageNet
[62, 62], but also large external datasets such as DinoV2 [39] and CLIP-OPENAI
[63]. We also consider the ScaleMAE [64] model, pretrained on satellite
imagery of various resolutions, and the model provided by Tolan metal [7] for
canopy height estimation from RGB images.

Input and Output Layer. As the models considered are originally designed for
the semantic segmentation of RGB images, we adapt their architecture to our
setting. To add the near-infrared channel, we change the input size from $3$
to $4$. We retain the pretrained weights related to RGB, and initialize the
near-infrared channel weights with small random values drawn from a normal
distribution $\mathcal{N}(0,0.01)$. We replace the class prediction layer with
a simple linear layer to predict continuous canopy heights and use the $L_{1}$
norm as a loss function.

Dataloader and Evaluation. During training, we sample random tiles of size
$224\times 224$. For inference, we sample tiles of the same size on the test
sets along a regular grid with $50\%$ overlap, and only retain the center half
of each prediction. We train our model to predict the canopy height for all
pixels, which may include heights that do not correspond to trees. However, we
only compute the evaluation metrics for pixels within the vegetation mask
described in Section 3.1.

Parameters and Resources. We use a batch size of $64$ with random scaling of
$0.5$ to $2$ and random rotations ($0$, $90$, $180$, or $270^{\circ}$). We use
the ADAM optimizer [65] with a learning rate of $10^{-3}$, a linear warmup of
$1$ epoch, and a ReduceLROnPlateau scheduler [66] with a patience of $1$ and a
decay of $0.5$. We perform early stopping with a patience of $3$. These
hyperparameters were selected by considering their impact on the UNet and ViT
models. Reproducing our experiments requires 1260 GPU-h with A100 GPUs. We
estimate our hyperparameters search and initial experiments to 1800 GPU-h. We
use a combination of internal clusters and the HPC GENCI.

whiteNwhite250m |  |  |  |  0m10m20m30m40m
---|---|---|---|---
|  |  |

(a) VHR image

(b) ALS height map

(c) UNet, MAE: 3.1

(d) PVTv2, MAE: 2.8

Figure 4: Qualitative Illustration. We select an area of interest and
represent the available VHR image (4(a)) and ground truth height map (4(b)),
as well as the predicted height map for UNet (4(c)) and PVTv2 (4(d)) models.
We report the MAE within the vegetation mask.

###  3.3 Results and Analysis

Setting. We evaluate the vision models presented in Table 2, as well as the
accuracy of existing canopy maps that we interpolate to our ground truth
resolution of $1.5$m per pixel. This evaluation, however, is subject to
limitations: (i) unknown training sets for some models might lead to data
contamination; (ii) interpolation might distort results, especially for the
map of Tolan _et al_. which we downsample from $1$m to $1.5$m; (iii) potential
discrepancies between the timing of the image used to predict the maps and our
ALS acquisitions, (iv) maps derived at lower resolution are trained to predict
the maximum canopy height in larger pixels, which bias them to higher values.
Such factors affect the reliability of comparisons with other maps, which are
given as indicative. By releasing all related data and metadata openly, we aim
to mitigate these issues for future evaluations.

Table 2: Canopy Height Prediction Models. We benchmark several backbone models for the task of predicting the canopy height of each pixel from a single satellite image. All models are pretrained on vision datasets and fine-tuned on our training set. Model | pretraining |  MAE |  nMAE |  RMSE |  Bias | Tree cov.
---|---|---|---|---|---|---
in m |  in % |  in m |  in m | IoU in %
UNet444github.com/qubvel/segmentation_models.pytorch [33] | ImageNet1k [67] |  2.67 |  23.8 |  14.18 |  -0.30 | 90.4
DeepLabv3111pytorch.org/vision [61] | ImageNet1k [67] |  3.18 |  28.4 |  14.83 |  -0.26 | 88.0
ViT-B333timm.fast.ai/ [34] | ImageNet21k [61] |  4.26 |  37.8 |  16.06 |  -0.84 | 86.0
HVIT333timm.fast.ai/ [34] | ImageNet21k [61] |  2.65 |  24.0 |  14.18 |  -0.13 | 90.2
PCPVT333timm.fast.ai/ [41] | ImageNet1k [61] |  2.57 |  23.1 |  14.06 |  -0.17 | 90.4
SWIN333timm.fast.ai/ [40] | ImageNet21k [61] |  2.54 |  22.8 |  14.00 |  -0.11 | 90.5
PVTv2333timm.fast.ai/ [12] | ImageNet1k [61] |  2.52 |  22.9 |  14.02 |  0.00 | 90.5
ScaleMAE 555github.com/bair-climate-initiative/scale-mae [64] | FotM [68] |  3.45 |  31.2 |  15.13 |  -0,48 | 88,2
ViT-B333timm.fast.ai/ [34] | DINOv2[39] |  4.84 |  43.2 |  16.68 |  -0.48 | 84.8
ViT-B222huggingface.co/laion [34] | CLIP_OPENAI [63] |  2.87 |  25.9 |  14.43 |  -0.07 | 89.7
ViT-L666github.com/facebookresearch/HighResCanopyHeight [34] | Tolan[7] |  4.46 |  38.9 |  16.27 |  -1.03 | 85.6

Metrics. We evaluate the performance of canopy height estimation models with
five metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE),
normalized MAE (nMAE)—which normalizes the absolute error by the actual
height, Bias—the error averaged across the test set, and Intersection over
Union (IoU) for Tree Cover predictions. The tree cover IoU is calculated by
comparing binary maps generated by thresholding both ground truth and
predicted height maps at a 2m threshold. All metrics are computed only on
pixels within the vegetation mask and with ground truth height below 60m. The
nMAE is calculated only for pixels with ground truth heights above 2m.

Analysis. We report the quantitative performance of all evaluated backbones in
Table 2, and selected illustrations in Figure 4. We make the following
observations:

  * •

Impact of Backbones. Contrary to trends in natural image analysis,
convolution-based approaches (UNet, HVIT) outperform ViTs, indicating that
convolutions can efficiently extract relevant local features. However,
hierarchical ViTs (SWIN, PCPVT, PVTv2) achieve the highest precision,
underscoring the multi-scale structure of the task.

  * •

Impact of Pretraining. Interestingly, models pre-trained on ImageNet (UNet,
PVTv2) perform better than foundation models trained on extensive databases of
natural images (CLIP, DINO). These models do not generalize well to canopy
height estimation, likely due to differences in viewpoint, task specificity,
data type, and available spectral bands. ScaleMAE and Tolan’s ViT, despite
being trained on satellite images, do not adapt well to our task. We
hypothesize that this is due to the spatial domain shift and the fact that
they are trained without the infrared channel.

  * •

Overall Performance. The methods assessed in this benchmark exhibit
commendable results, achieving tree cover detection with over 90% IoU and an
nMAE around 20% for the best-performing models.

Comparison with Existing Maps. In Table 3, we evaluate the precision of canopy
height maps generated by UNet and PVTv2 networks trained on the Open-Canopy
dataset against those from other research. With the caveats on the fairness of
the comparison mentioned above, our maps achieve significantly better
precision. The low performance of models derived from low-resolution imagery
is expected, as they are trained to estimate tree height at a different
resolution. Among the ALS-based methods, Liu _et al_.’s model performs best,
likely due to its training data from Europe, which differs from Tolan et al.’s
training in the continental US. Moreover, the Tolan _et al_. model relies
solely on RGB data, while the inclusion of near-infrared is proven to be
highly discriminative for vegetation analysis [69]. In Figure 5, we report
error plots across various vegetation height bins, highlighting that the PVTv2
model trained on Open-Canopy exhibits significantly lower bias and superior
performance, especially in areas with tall trees.

Table 3: Canopy Height Maps Evaluation. We evaluate on our test set several of the available canopy height map products obtained by previous works. Map | Backbone | res. |  MAE |  nMAE |  RMSE |  Bias | Tree cov.
---|---|---|---|---|---|---|---
in m |  in m |  in % |  in m |  in m | IoU in %
Potapov [11] | UNet | 30 |  6.27 |  58.1 |  18.68 |  -1.79 | 78.0
Schwartz [9, 70] | UNet | 10 |  5.17 |  42.7 |  7.20 |  -3.37 | 76.8
Lang [10] | CNN | 10 |  9.22 |  89.5 |  17.14 |  -8.40 | 77.4
Liu [8] | UNet | 3.0 |  4.83 |  46.6 |  16.90 |  -1.56 | 84.1
Tolan [7] | ViT-L | 1.0 |  5.07 |  43.7 |  17.15 |  -2.95 | 78.8
Open-Canopy | UNet | 1.5 |  2.67 |  23.8 |  14.18 |  -0.30 | 90.4
Open-Canopy | PVTv2 | 1.5 |  2.52 |  22.9 |  14.02 |  0.00 | 90.5
$0$$10$$20$$30$Proportion (in
%)0-2m2-5m5-10m10-15m15-20m20-30m30-60m$-20$$0$$20$Ground truth heightError
(in m)Open-Canopy (1.5m)Tolan (1m)Liu (3m)Schwartz (10m) Figure 5:
Distribution of Error. We plot the distribution of errors according to the
ground truth canopy height for a PVTv2 model trained on Open-Canopy and
different canopy height map products.

###  3.4 Limitations

Our dataset, while robust, has several limitations:

  * •

Geographic Focus: Although metropolitan France offers a unique combination of
open-access data and diverse landscapes, the geographic focus of Open-Canopy
limits its global benchmark for VHR canopy height prediction. In particular,
it lacks critical forest types such as rainforests. We hope that France’s
open-access policy and this benchmark will inspire other countries and leads
to the creation of a truly global VHR canopy height dataset.

  * •

Limits of ALS: The ALS-based canopy heights are derived from physical
measurements using aerial LiDAR, which can introduce errors, such as those
from multiple echoes. These errors are mitigated by LiDAR-HD’s outlier
removal, but cannot be excluded without costly in-situ drone-based
measurements. Moreover, while SPOT 6-7 images are from spring and summer,
LiDAR measurements are from all seasons.

  * •

Biomass Estimation: Canopy height alone is an imperfect proxy for biomass as
it does not take into account the multi-layered structure and density of tree.

##  4 Open-Canopy-$\Delta$: Detecting Canopy Height Changes

In this section, we present Open-Canopy-$\Delta$, our auxiliary benchmark for
the task of canopy height change detection. We first present the dataset
(Section 4.1), then our results Section 4.2

###  4.1 Dataset Characteristics

We introduce Open-Canopy-$\Delta$, a new benchmark for detecting vegetation
change between consecutive VHR images. We define changes as areas in which the
canopy height has significantly decreased.

Extent and Context. We study the Forêt de Chantilly, a declining forest due to
climate change and of high concern for conservationists [71]. We consider two
ALS acquisitions in February 2022 [31] and September 2023 [71], allowing us to
build their respective height maps and collect their associated SPOT 6-7
images. In total, the spatial extent of the area of interest is $166,340$
hectares. Note that this area does _not_ overlap with the training set of
Open-Canopy.

Processing. We first construct a rasterized canopy change map by subtracting
the first ALS-based height map from the later one. Change in canopy height can
be due to forest disturbance events, such as fires, clear or partial cuts, or
maintenance pruning. However, other unwanted elements can affect this
measurement, such as seasonal growth cycles, wind, or sensor errors. To
generate robust binary _change mask_ , we focus on areas with significant,
localized, and consistent decreases in canopy height. In practice, the canopy
change masks are obtained as follows: (i) we select pixels showing a loss
exceeding $10$m, (ii) we regularize the resulting binary masks with
morphological operations: erosion with a kernel of 3-pixel edge, two
iterations dilation with a 3-pixel edge kernel, and finally erosion with a
3-pixel edge kernel, and (iii) we remove connected components smaller than
$100$m2. See Figure 6 for illustrations and the supplementary for more details
on the choice of hyperparameters.

###  4.2 Results and Analysis

We evaluate different approaches for the task of estimating the difference in
canopy height between two VHR images, which holds significant applications in
forestry management.

Setting. As shown in Figure 6, we provide models with two VHR satellite images
corresponding to two consecutive years. We generate a canopy change map by
subtracting the canopy height maps predicted by our model from these two
images. We do not directly compare the change maps, as the precision of canopy
height estimation can be larger than normal tree growth. Instead, we apply the
preprocessing described in Section 4.1 to produce predicted binary canopy
height change masks.

Metrics. We evaluate the predicted canopy height change masks by computing the
pixel-wise Precision, Recall, F1 score, and IoU with respect to the ALS-
derived masks. Results. We compare height change masks predicted by a PVTv2
model trained on Open-Canopy with those derived from height maps provided by
[9] and Global Forest Change [42]. As detailed in Table 4, our model achieves
significantly better performance than other methods, although precision
remains a challenge. Figure 6 illustrates that while our predicted change maps
do not perfectly align with the ground truth maps—which also suffer from minor
registration errors—the consistency of our predictions suggests their
potential utility in detecting significant year-to-year changes.

|  |  |  |  |
---|---|---|---|---|---
|  |  |  |  |  -20m-15m-10m-5m0m+1m+2m

N50m (a) VHR year 2022

(b) ALS change map

(c) Sentinel change map

(d) ALS change mask

(e) Sentinel change mask

(f) VHR year 2023

(g) Our change map

(h) GFC change map

(i) Our change mask

(j) GFC change mask

Figure 6: Canopy Height Change. We consider VHR images taken in 2022 and 2023 in Chantilly Forest (6(a) and 6(f)), and use ALS observations of the same years to derive a canopy height change map (6(b)). We compare this map to the ones predicted by a PVTv2 model (6(g)) and two competing approaches: Sentinel-derived maps from Schwartz [9] (6(c)) and Global Forest Change [42] (6(g)). Finally, we compare the binary change masks derived from ALS measurements (6(d)) and from predicted change maps (6(e),6(i),6(j)). Table 4: Forest Change Mask Evaluation. We evaluate our best model (PVTv2) for the task of detecting canopy height change masks. We also evaluate canopy height maps from  | Precision (%) | Recall (%) | F1 score (%) | IoU (%)
---|---|---|---|---
Schwartz [9] | 32.7 |  44.6 |  38.0 |  24.2
Global Forest Change [42] |  30.6 |  45.4 |  31.0 |  20.5
Open-Canopy (ours) | 29.5 | 48.8 | 36.8 | 22.5

##  5 Conclusion

We introduced Open-Canopy, the first country-scale, open-access benchmark
combining VHR satellite imagery with ALS-derived canopy height measurements.
We evaluated multiple recent state-of-the-art computer vision models for the
task of canopy height estimation. Despite the dominance of convolutional
networks in prior works, our findings suggest that transformer-based
architectures exhibit superior performance. We also proposed Open-
Canopy-$\Delta$, a benchmark for detecting canopy height change detection from
consecutive observations, a difficult task, even for the best-performing
models. We hope that our dataset and benchmarks will encourage the computer
vision community to further explore canopy height estimation as a standard
task for evaluating new architectures and inspire forestry experts to design
bespoke architectures.

##  6 Acknowledgment

The experiments conducted in this study were performed using HPC/AI resources
provided by GENCI-IDRIS (Grant 2023-AD010114718 and 2023-AD011014781) and
Inria.

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## Appendix

##  A-1 Additional Evaluations

We present several supplementary experiments to evaluate the impact on the
performance of the tree height (Section A-1.1), the spatial resolution of the
evaluation (Section A-1.1), and the parameters of the canopy height change
processing (Section A-1.3).

###  A-1.1 Influence of Tree Height

We provide in Table A-1 an evaluation of canopy height estimation for
different ranges of height.

  * •

Note that the nMAE (normalized Mean Absolute Error) is computed for all ranges
as the average of the pixel-wise normalized absolute error:

$\displaystyle\text{nMAE}=\frac{\left|(z_{\text{true}}-z_{\text{pred}})\right|}{1+z_{\text{true}}}~{},$
(A-1)

where $z_{\text{true}}$ and $z_{\text{pred}}$ are respectively the ALS-derived
and predicted height for a given pixel. The additional term in the denominator
makes this measure more robust for pixels corresponding to low vegetation.

  * •

When computing the nMAE for the 0-60 m range, we actually exclude the 0-2m
bin. Indeed, even with the regularization, these values tend to be very large
and dominate the metric. Furthermore, it makes the comparison unfair for maps
with lower spatial resolution which aim to predict the highest pixel in a
larger pixel, and may overlap with bare soil pixel at a higher resolution.

As shown in Table A-1 by the bias of our model for different ranges, our model
tends to over-predict the height of small trees and under-predict the height
of tall trees. While the average error is higher for larger trees, our model
has the lowest nMAE for the 20-30m range, with a value of $12.1\%$.

Table A-1: Canopy Height Prediction Per Height Bins. We report the metrics for different bins of true tree height for the PVTv2[12] model. Range in m |  0-2 |  2-5 |  5-10 |  10-15 |  15-20 |  20-30 |  30-60 | 0-60
---|---|---|---|---|---|---|---|---
MAE in m |  11.67 |  2.29 |  2.65 |  12.70 |  -12.61 |  -13.00 |  -15.52 |  -2.52
nMAE in % |  138.8 |  53.6 |  32.1 |  120.3 |  -114.3 |  -112.1 |  -116.0 |  -22.9
RMSE in m |  14.31 |  3.67 |  3.69 |  13.60 |  -13.53 |  -14.19 |  -17.56 |  -4.02
Bias in m |  11.49 |  0.87 |  0.65 |  10.21 |  1-0.42 |  1-1.90 |  1-5.31 |  -0.00
Tree cov. IoU (%) |  13.- |  72.6 |  96.5 |  199.3 |  -199.7 |  -199.8 |  -199.6 |  -90.5

###  A-1.2 Evaluation at a resolution of 10m

In order to provide a more fair evaluation for the models predicting canopy
height at a $10$ m resolution, we convert both our ground truth and predicted
height map to a resolution of $10$m and re-evaluate all available maps. We
down-sample the height maps by taking for each 10m resolution pixel the
maximum value of all $1.5$m pixels it overlaps. This is equivalent to
rasterizing the full ALS 3D point cloud to a $10$m grid directly. We take the
maximum value to be comparable to models that are trained on GEDI RH100 or
RH95 (relative height at the 100th or 95th percentile).

We report the results in Table A-2, and observe a similar ordering than in
Table 6 of the main paper. All methods see improved metrics as the problem is
simpler, except for Tolan. In particular, the tree coverage problem becomes
significantly easier at this resolution, with all $10$ m-resolution methods
nearing $90$% IoU.

Note that the height map of [8] at a resolution of 3m was provided by the
authors and is not available online.

Table A-2: Canopy Height Prediction at 10m resolution. We resample all ground truth and predicted maps at on a $10$ m grid. Map | Backbone | Initial res. |  MAE |  nMAE |  RMSE |  Bias | Tree cov.
---|---|---|---|---|---|---|---
in m |  in m |  in % |  in m |  in m | IoU in %
Potapov [11] | UNet | 30 |  6.17 |  44.6 |  18.33 |  -3.31 | 80.2
Schwartz [9, 70] | UNet | 10 |  4.00 |  26.9 |  15.28 |  -1.38 | 90.1
Lang [10] | CNN | 10 |  8.64 |  92.9 |  29.25 |  -6.27 | 90.1
Liu [8] | UNet | 3.0 |  4.58 |  37.4 |  10.97 |  -1.26 | 88.2
Tolan [7] | ViT-L | 1.0 |  6.10 |  42.1 |  17.95 |  -5.37 | 81.6
Open-Canopy | UNet | 1.5 |  2.72 |  19.0 |  13.95 |  -2.06 | 93.4
Open-Canopy | PVTv2 | 1.5 |  2.42 |  17.6 |  13.57 |  -1.69 | 93.3

###  A-1.3 Parameters of the Change Detection

We provide in Table A-3 an evaluation of canopy height change detection for
different configurations of the ground truth binary change map. In particular,
we consider the impact of the minimum height difference to be considered as a
changed pixel, and the minimum contiguous change area below which we discard
the components. Naturally, when only keeping large change areas, the problem
becomes easier. The influence of the minimal tree height change is more
ambiguous, as for large values, it implies precisely detecting the height of
heigher trees, which is more difficult. Overall, while our choice of
parameters (10 m and 100 m2) is arbitrary, it corresponds to changes that can
be visually detected between two images, see Figure A-2.

##  A-2 Additional Illustrations

We provide here additional illustrations for qualitative assessment.

###  A-2.1 Canopy Height

Table A-3: Canopy Height Change Detection We compute the IoU metric (in %) for various minimum height difference (row, in m) and minimum contiguous area of change (column, in m2). The values chosen in the benchmark are underlined. min diff min surf | 10 m2 | 25 m2 | 100 m2 | 200 m2 | 300 m2 | 400 m2
---|---|---|---|---|---|---
-5 m | $\pagecolor[rgb]{0.78948, 0.1579, 0.07895}7.0$ | $\pagecolor[rgb]{0.79079, 0.16316, 0.08158}7.1$ | $\pagecolor[rgb]{0.7921, 0.16842, 0.08421}7.2$ | $\pagecolor[rgb]{0.77894, 0.11578, 0.05789}6.2$ | $\pagecolor[rgb]{0.76711, 0.06842, 0.03421}5.3$ | $\pagecolor[rgb]{0.75394, 0.01578, 0.00789}4.3$
-10 m | $\pagecolor[rgb]{0.92237, 0.68948, 0.34474}17.1$ | $\pagecolor[rgb]{0.9329, 0.73158, 0.36579}17.9$ | $\pagecolor[rgb]{0.99342, 0.97368, 0.48684}22.5$ | $\pagecolor[rgb]{0.97368, 0.98684, 0.48684}23.5$ | $\pagecolor[rgb]{0.89474, 0.94737, 0.44737}25.0$ | $\pagecolor[rgb]{0.70526, 0.85263, 0.35263}28.6$
-15 m | $\pagecolor[rgb]{0.98552, 0.9421, 0.47105}21.9$ | $\pagecolor[rgb]{0.99474, 0.99737, 0.49737}23.1$ | $\pagecolor[rgb]{0.7421, 0.87105, 0.37105}27.9$ | $\pagecolor[rgb]{0.40526, 0.70263, 0.20263}34.3$ | $\pagecolor[rgb]{0.17368, 0.58684, 0.08684}38.7$ | $\pagecolor[rgb]{0.03684, 0.51842, 0.01842}41.3$
-20 m | $\pagecolor[rgb]{0.93948, 0.7579, 0.37895}18.4$ | $\pagecolor[rgb]{0.95658, 0.82632, 0.41316}19.7$ | $\pagecolor[rgb]{0.6579, 0.82895, 0.32895}29.5$ | $\pagecolor[rgb]{0.33158, 0.66579, 0.16579}35.7$ | $\pagecolor[rgb]{0.63158, 0.81579, 0.31579}30.0$ | $\pagecolor[rgb]{0.55264, 0.77632, 0.27632}31.5$

See Figure A-1 for a comparison between the ALS-derived height map and the
height map predicted from SPOT images. Our model is able to accurately predict
the vegetation height in a variety of scenarios such as mountainous areas
(first row), and detect small hedges on agricultural lands (second row). Our
model can also handle dense forests (row 3 and 4) and urban environments (row
5), as well as mixed scenes (row 6 and 7). The high resolution of our
predictions also allows for the identification of man-made features like
forest paths, crucial for forest management.

1 |  |  |  |  whiteNwhite250m |  0m10m20m30m40m
---|---|---|---|---|---
2 |  |  |  |
3 |  |  |  |  |
4 |  |  |  |  |
5 |  |  |  |  |
6 |  |  |  |  |
7 |  |  |  |  |
|  |  |  |  |

(a) VHR image

(b) Vegetation mask

(c) ALS height map

(d) PVTv2 height map

Figure A-1: Canopy Height Estimation Illustrations. We select seven areas of
interest and represent the available VHR image (1(a)), the vegetation mask
used for evaluation (1(b)), the ground truth ALS-derived height map (1(c)),
and the height map estimated with PVTv2 model from the VHR image (1(d)). Scale
and orientation are shared across all subfigures.

###  A-2.2 Canopy Height Change

See Figure A-2 for more illustrations of height change detection. While our
model tends to over predict small growth or loss of canopy height, the areas
of strong disturbances—as denoted by our smoothed and filtered binary change
maps—are overall well detected and delineated. Our illustration covers areas
of dense forests (first row) and mixed scenes (row 2 and 3). Our method can
detect disturbances such as clear and selective cuts.

Note that the Sentinel-derived height maps for 2022 and 2023 were provided by
the authors of [9], as only the map for 2020 is available online.

|  |  |  |
---|---|---|---|---
|  |  |  |  -20m-15m-10m-5m0m+1m+2m
|  |  |
|  |  |  |
|  |  |  |
|  |  |  |

whiteNwhite100m (a) VHR year 2022

(b) ALS change map

(c) Our change map

(d) Sentinel change map

(e) VHR year 2023

(f) ALS change mask

(g) Our change mask

(h) Sentinel change mask

(a) VHR year 2022

(b) ALS change map

(c) Our change map

(d) Sentinel change map

(e) VHR year 2023

(f) ALS change mask

(g) Our change mask

(h) Sentinel change mask

(a) VHR year 2022

(b) ALS change map

(c) Our change map

(d) Sentinel change map

(e) VHR year 2023

(f) ALS change mask

(g) Our change mask

(h) Sentinel change map

Figure A-2: Canopy Height Change. We consider VHR images taken in 2022 and
2023 in Chantilly Forest (2(a) and 2(e)), and use ALS observations of the same
years to derive a canopy height change map (2(b)). We compare this map to the
ones predicted by a PVTv2 model (2(c)) and by a model from Schwartz trained on
Sentinel data [9]. We also compare the binary change masks derived from ALS
measurements (2(f)) and from predicted change maps (2(g) and 2(h)). Scale and
orientation are shared across all subfigures.

##  A-3 Dataset description

###  A-3.1 Access

  * •

The dataset is hosted on Huggingface platform
(https://huggingface.co/datasets/fajwel/Open-Canopy), with download and usage
instructions on the Open-Canopy project page hosted on GitHub
(https://github.com/fajwel/Open-Canopy).

  * •

The data is governed by the Open License 2.0 of Etalab
(https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf).

  * •

Codes for data preprocessing, training a custom model and evaluation are
available at https://github.com/fajwel/Open-Canopy. The code for training all
models of the benchmark will be released in the coming months along with their
pretrained weights.

###  A-3.2 Composition

We describe here the organization of the dataset. See Section A-4 for details
on how the dataset was prepared.

The dataset is organized in the following way:

  * •

The folder canopy_height contains data for canopy height estimation.

  * •

The folder canopy_height_change contains data for canopy height change
estimation.

The composition of the canopy_height folder is the following:

  * •

The file geometries.geojson stores a list of 95,429 $1km^{2}$ square
geolocated geometries, giving access to the splits of the dataset. It can be
loaded using the python package geopandas 111https://geopandas.org/en/stable/.
Each geometry designates either a train, validation, test or buffer area. This
information is stored in the column split. There are 8,046 buffer tiles,
66,339 train tiles, 7,369 validation tiles and 13,675 test tiles.
Additionally, each geometry is associated to a year (corresponding to the year
of the corresponding LiDAR acquisition), stored in the column lidar_year.

  * •

The file forest_mask.parquet stores geolocated geometries of forests’
outlines. It can be loaded using the python package geopandas. The parquet
format is used to accelerate loading time.

  * •

Each folder 2021, 2022 and 2023 contains three files:

    * –

lidar.vrt is a geolocalized virtual file that gives access to SPOT 6-7 images
stored in the subfolder spot. It can be accessed through Qgis software
222https://www.qgis.org/en/site/ or python rasterio library
333https://rasterio.readthedocs.io/en/stable/ for instance. It has the same
extent as the geometries of the associated year.

    * –

Similarly lidar.vrt gives access to ALS-derived (LiDAR) canopy height maps
stored in the subfolder lidar.

    * –

Similarly lidar_classification.vrt gives access to classification rasters
stored in the subfolder lidar_classification.

The composition of the canopy_height_change folder is the following:

  * •

The file spot_1.tif is a geolocalized image extracted from SPOT 6-7 images in
the year 2022 in the area of Chantilly, France.

  * •

The file spot_2.tif is a geolocalized image extracted from SPOT 6-7 images in
the year 2023 in the area of Chantilly (France).

  * •

The file lidar_1.tif is a geolocalized ALS-derived height map in the year 2022
in the area of Chantilly (France), derived from LiDAR HD [31].

  * •

The file lidar_2_m.tif is a geolocalized ALS-derived height map in the year
2023 in the area of Chantilly (France), provided by [71], at a resolution of
1m, with height in meters, and covering only forests.

  * •

The file pred_1.tif is a geolocalized height map predicted by a PVTv2 model in
2022 in the area of Chantilly (France).

  * •

The file pred_2.tif is a geolocalized height map predicted by a PVTv2 model in
2023 in the areao of Chantilly (France).

  * •

The file lidar_classification.tif is an ALS-derived classification raster in
2022 in the area of Chantilly (France).

  * •

Additionally, files that follow the following pattern *_masked.tif designate
images masked on the extent of the available ALS data for 2023.

###  A-3.3 Characteristics

  * •

We provide SPOT 6-7 images, ALS-derived height maps and classification rasters
covering 95,429 km2 (including a "buffer" area of 8046 km2, a train area of
66,339 km2, a validation area of 7,369 km2 and a test area of 13,675 km2).
Each image has a resolution of 1.5m, with one annotation per pixel, for a
total of 42,455,312,381 annotations.

  * •

Additionally, we provide SPOT 6-7 imagery, ALS-derived height maps and a
classification raster on the Chantilly forest area for 2022 and 2023 (571
km2).

  * •

The Open-Canopy dataset is derived from a larger dataset of SPOT 6-7
acquisitions across the full metropolitan French territory between 2013 and
2023 444https://openspot-dinamis.data-terra.org, and a larger dataset of ALS
acquisitions from the IGN campaign that started in 2021 and aims at covering
the full metropolitan French territory (LiDAR HD)
555https://geoservices.ign.fr/lidarhd. The Open-Canopy dataset focuses on
domains that are representative of the diversity of French forests and where
LiDAR HD is available at the time of writing, with the goal of limiting the
dataset’s size to approximately 300 GB, in order to facilitate its usage by
the machine learning community.

  * •

Each SPOT image is at a resolution of $1.5$ m per pixel, and features $4$
spectral channels: red, blue, green, and near-infrared.

  * •

Each height map image is at a resolution of $1.5$ m per pixel, and features 1
channel (height in decimeters except if notified in the filename in the
following format: "<name>_<unit>.tif").

  * •

Each classification image is at a resolution of $1.5$ m per pixel, and
features 1 channel (classification
666https://geoservices.ign.fr/sites/default/files/2023-10/DC_LiDAR_HD_1-0_PTS.pdf
for a description of classes). Forests’ outlines are stored as geometries in a
parquet file. A Python utility is provided to create a vegetation mask from
the classification raster and the forests’ outlines.

##  A-4 Dataset preparation

###  A-4.1 SPOT 6-7 satellite imagery

  * •

The aerial images are sampled from the DINAMIS 777https://openspot-
dinamis.data-terra.org collection. This collection consists of an annual
mosaic of selected tiles taken by SPOT 6-7 satellites between March and
October of each year between 2013 and 2023, covering the entire French
metropolitan territory. All images are orthorectified by IGN and mapped onto a
unified cartographic coordinate reference system (Lambert 93). Each tile
consists of an image with four spectral bands: red, green, blue, and near-
infrared at a resolution of $6$m, and an image with one panchromatic band at a
resolution of $1.5m$ that can be downloaded separately.

  * •

A total of 52 pairs of spectral and panchromatic images were downloaded from
the DINAMIS website, for each year from 2021 to 2023, to cover a very diverse
range of forest types in areas where LiDAR HD was available at the time of the
creation of the dataset.

  * •

We applied pansharpening with the weighted Brovey algorithm [59] to upsample
all four spectral bands to a resolution of $1.5$m, resulting in one image with
four bands for each tile.

  * •

We cropped each image to the area covered by the ALS acquisitions of the same
year.

  * •

Pixels values were clipped to a maximum value of 2000 to avoid outliers (upper
bound both quantitatively and qualitatively assessed through histograms and
visualization).

  * •

Resulting images were normalized to a 0-255 range and saved as uint8 in a
block-tiled compressed tiff format ($256\times 256$).

  * •

The pansharpening and normalization procedures were voluntarily kept
relatively simple in order to facilitate reproducibility. They may not be
optimal for visualization, _e.g_., lacking harmonization, but we expect deep
learning models to be robust to such variations in input data.

###  A-4.2 ALS data

  * •

The ALS classified point clouds were downloaded from the LiDAR HD website
(IGN). A reference to each download link is saved in the file
geometries.geojson in the column lidar_url.

  * •

For each geometry, canopy height images were derived from ALS data by taking
the maximum difference between the height of each point and the one of its
nearest point classified as ground within its pixel, interpolating values in
areas without data.

  * •

LiDAR point clouds were classified by IGN into the main types of land cover
(water, ground, high vegetation over 1.5m, buildings…). We use this
classification to produce classification rasters at a resolution of 1.5m,
where each pixel takes the value of the most frequent class of the
corresponding LiDAR points.

  * •

We then create vegetation masks by taking the union of the ALS-derived mask
indicating vegetation over 1.5m in height, with the official forest plots
outlines (file forest_mask.parquet), both provided by IGN. The resulting
vegetation masks cover trees and shrubs within forest plots as well as
outside, such as hedges and urban trees.

  * •

The official forests’ outlines were extracted from “BD foret”
888https://geoservices.ign.fr/bdforet#telechargementv2 and "simplified" using
geopandas python library to a precision of 10m, with the goal to limit their
size.

###  A-4.3 Splits

Our sampling strategy is semi-automated and proceeds as follows:

  * •

SPOT images were associated to LiDAR height maps of the same year and
geolocation (each LiDAR height map corresponds to a 1km2 geolocalized square
tile, referred to as “geometry” in the following).

  * •

Geometries on overlapping areas between spot full images were removed.

  * •

Geometries that had more than 100 zeros on the first spot band (_e.g_., on
edges of a full spot image) were discarded to avoid tiles with missing data.

  * •

Test geometries of 1km2 were sampled (with a fixed seed) to form contiguous
squares of 7km2 and to cover 20,000 km2.

  * •

Test geometries that overlapped each other were dropped.

  * •

Test geometries that covered different years in terms of LiDAR acquisitions
were dropped.

  * •

This process resulted in a total test area of 13,675 km2.

  * •

A buffer of 1$km$ was applied around each test area of 7km2.

  * •

Validation and train geometries were randomly sampled (with a fixed seed)
among the remaining geometries, with a proportion of 10% for validation and
90% for training.

  * •

This process resulted in a training area of 66,339 km2 and a validation area
of 7,369 km2.