Organizations: Engineering Department, University of Naples Parthenope, Naples, Italy · INRIA, Grenoble, France · CNRS Délégation Alpes: Grenoble, Auvergne-Rhône-Alpes, France · Engineering Department, University of Sannio, Benevento, Italy
Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS). Their delineation is challenging because of high planting density, short rotation cycles, visual confusion with surrounding vegetation, the availability of dense labels for one reference year only, and severe class imbalance at the landscape scale. Although Deep Learning (DL) methods have shown strong potential for vegetation mapping, existing approaches are typically designed for forests, generic plantation systems, or orchards, and do not explicitly address the structural specificity and hard-negative confusion that characterize Christmas tree plantations. In response to these challenges, this work makes three main contributions: (i) it frames Christmas tree plantation mapping as a distinct rare-target semantic segmentation problem; (ii) it introduces a Hard Negative Mining (HNM) strategy to improve discrimination against confusing background patterns; and (iii) it evaluates the proposed framework across complementary levels, including supervised testing, temporal transfer, and large-scale validation. On the 2020 test set held out, the best model, DeepLabV3 with a ResNet-34 encoder, achieves an IoU of 0.733 and an F1-score of 0.846. HNM substantially improves precision-recall behavior, increasing the area under the precision-recall curve from 0.204 to 0.913. Temporal inference further shows meaningful transferability, reaching IoU/F1 values of 0.751/0.858 on 2017/2018 and 0.691/0.817 on 2023. Large-scale validation further highlights the intrinsic difficulty of the task, as Christmas tree plantations occupied only a very small fraction of the extent of the common evaluation, corresponding to 1,498.4 ha (1.72%) in 2017/2018 and 1,782.2 ha (2.04%) in 2023 out of 87,309.4 ha in total.
Figures & tables
Figure 1: Examples of the high visual and structural variability of Christmas tree plantations in high-resolution RGB imagery. Despite belonging to the same target class, parcels exhibit drastic differences depending on their management phase and spatial context: (a) newly established plantation on bare soil; (b) variable density and row spacing across different sub-plots; (c) mature and densely planted parcel with strong linear patterns; (d) irregular harvesting and clearing dynamics; (e) sparse canopy cover with grassy background; (f) early-stage planting adjacent to forest borders.
Figure 2: Study area in the Morvan Regional Natural Park, France. The figure shows the geographical context of the Morvan area within France, the extent of the protected geographical indication “Christmas tree of the Morvan”, the parcel-level inventory of Christmas tree plantations mapped in 2020 (red), and the spatial extent of the orthophoto tiles used in this study (blue dashed rectangle).
Data
Year(s)
Resolution
Source
Reference
Role in workflow
BD ORTHO orthophotos
2017/2018, 2020, 2023
0.2 m
IGN
[ 37 ]
Main RGB imagery for supervised training (2020), temporal inference (2017/2018 and 2023), and visual interpretation.
Manual parcel annotations
2020
Parcel level
Based on expert photo-interpretation and field verification
[ 8 ]
Positive reference layer for Christmas tree plantations; used for training, validation, and test construction.
OSO land-cover map
2018, 2020
10 m
THEIA
[ 38 , 39 ]
Water masking for 2017/2018 and 2020; exclusion of obvious non-target areas.
CLC+ Backbone
2023
10 m
Copernicus Land Monitoring Service
[ 40 ]
Water masking for the 2023 temporal inference dataset.
Natural grassland dataset
2020
10 m
Panhelleux et al.
[ 31 ]
Grassland-related hard negative support for 2020; identification of herbaceous areas likely to be confused with Christmas tree plantations.
HRL Grassland
2017, 2023
10 m
Copernicus Land Monitoring Service
[ 41 , 42 ]
Grassland support layers for temporal inference in 2017/2018 and 2023.
Table I: Summary of the main datasets used in this study, their temporal coverage, and their role in the workflow.
Figure 3: Overview of the proposed workflow for Christmas tree plantation delineation from high-resolution RGB imagery. The pipeline combines BD ORTHO orthophotos, parcel-level reference data, and ancillary land-cover layers to construct a hard-negative-aware training dataset. Positive and hard negative samples are then used to train the semantic segmentation model, which is evaluated both on the 2020 held-out test set and in temporal transfer settings over 2017/2018 and 2023 through visually guided refinement and large-scale analysis.
Figure 4: Land-cover composition inside the verified Christmas tree parcels (2020 dataset). The bar chart illustrates the raw spatial overlap between the manually annotated parcels and the ancillary layers (grassland, clear-cut, and water), motivating the use of explicit priority rules during multi-class mask generation.
Figure 5: Qualitative comparison on the 2020 test set. Columns show (from left to right) RGB patch, GT 2020 multiclass mask, GT binary mask (Christmas tree class), and predictions from DeepLabV3-R18, DeepLabV3-R34, DeepLabV3-R50, FPN-R34, U-Net (b16), and U-Net++-R34 (thresholds as in Table II ).
Model
thr
IoU ↑
F1 ↑
Prec. ↑
Rec. ↑
DeepLabV3-R18
0.80
0.703
0.826
0.830
0.821
DeepLabV3-R34
0.70
0.733
0.846
0.866
0.827
DeepLabV3-R50
0.80
0.697
0.822
0.807
0.837
FPN-R34
0.75
0.700
0.824
0.833
0.814
U-Net (b16)
0.55
0.512
0.677
0.762
0.609
U-Net++-R34
0.75
0.731
0.845
0.843
0.846
Table II: Performance on the 2020 test set. Thresholds (thr) are selected on the validation set by maximizing the F1-score. Best values are highlighted in bold. Arrows indicate whether higher ( ↑ ) values correspond to better performance.
Loss
IoU ↑
F1 ↑
Prec. ↑
Rec. ↑
Tversky
0.577 ± 0.067
0.730 ± 0.054
0.655 ± 0.053
0.826 ± 0.058
Weighted BCE
0.667 ± 0.042
0.800 ± 0.031
0.769 ± 0.102
0.849 ± 0.081
Weighted BCE + Tversky
0.695 ± 0.009
0.820 ± 0.006
0.756 ± 0.017
0.895 ± 0.011
Table III: Multi-seed loss ablation on DeepLabV3-R34 over the 2020 test set with fixed threshold 0.70. Values are reported as mean ± standard deviation across seeds. Best values are highlighted in bold.
Figure 6: Examples of visual comparison for DeepLabV3–R34 trained with and without Hard Negative Mining (HNM) on 2020 test patches. Columns: RGB; GT (multiclass) over RGB; no-HNM mask over RGB; no-HNM P(CT) ; HNM mask over RGB; HNM P(CT) (shared [0,1] scale). HNM suppresses diffuse false activations and produces more spatially coherent plantation responses.
Training regime
AP
AUPRC
no-HNM
0.2042
0.2044
HNM
0.9125
0.9127
Table IV: Threshold-free performance on the hard-negative 2020 test split (DeepLabV3–R34). AP corresponds to the area under the Precision–Recall curve (AUPRC).
Figure 11
Year
IoU ↑
F1 ↑
Precision ↑
Recall ↑
2023
0.691
0.817
0.876
0.766
2017/2018
0.751
0.858
0.893
0.826
Table V: Temporal inference results on the refined GT on the fixed 2020 test patch locations. Metrics are computed for the HNM pipeline.
Figure 9: Qualitative evaluation of the large-scale detection framework over the Morvan Regional Natural Park for the 2017/2018 and 2023 evaluation years. The main panels display the wall-to-wall spatial distribution of the predicted Christmas tree plantations against their respective ground-truth inventories across the entire 87,000 ha extent. The side panels provide representative zoomed-in areas detailing the local matching behavior: solid yellow masks indicate the model predictions, while red boundaries denote the reference ground-truth polygons. The selected insets highlight the model’s accuracy and temporal consistency across different spatial contexts, including dense central clusters and isolated peripheral regions.
Metric
2023
2017/2018
GT area (ha)
1,782.21
1,498.44
Pred. area (ha)
2,006.63
2,206.52
FP (ha)
695.81
889.80
FN (ha)
471.39
181.72
Precision
0.653
0.597
Recall
0.736
0.879
Table VI: Final large-scale area-based validation over the common study extent after the selected post-processing step.
Department of Forest Resources and Environmental Conservation, Virginia Tech, Virginia, USA. · Department of Computer Science, Virginia Tech, Virginia, USA
Osnabrück University, Institute for Computer Science, Remote Sensing Osnabrück, Germany · Leibniz Institute for Agricultural Engineering and Bioeconomy, System Process Engineering, Germany · Osnabrück University, Institute of Cognitive Science, Artificial Intelligence, Germany +1