SelvaBox: A high‑resolution dataset for tropical tree crown detection
Authors: Hugo Baudchon
Arthur Ouaknine
Martin Weiss
Mélisande Teng
Thomas R. Walla
Antoine Caron-Guay
Christopher Pal
Etienne Laliberté
Venue: ICLR 2026 Poster (2026)
Keywords: Remote sensing
Forest monitoring
Tree crown detection
Tropical forest dataset
Primary area: datasets and benchmarks

Abstract: Detecting individual tree crowns in tropical forests is essential to study these complex and crucial ecosystems impacted by human interventions and climate change. However, tropical crowns vary widely in size, structure, and pattern and are largely overlapping and intertwined, requiring advanced remote sensing methods applied to high-resolution imagery. Despite growing interest in tropical tree crown detection, annotated datasets remain scarce, hindering robust model development. We introduce SelvaBox, the largest open‑access dataset for tropical tree crown detection in high-resolution drone imagery. It spans three countries and contains more than $83\,000$ manually labeled crowns -- an order of magnitude larger than all previous tropical forest datasets combined. Extensive benchmarks on SelvaBox reveal two key findings: 1) higher-resolution inputs consistently boost detection accuracy; and 2) models trained exclusively on SelvaBox achieve competitive zero-shot detection performance on unseen tropical tree crown datasets, matching or exceeding competing methods. Furthermore, jointly training on SelvaBox and three other datasets at resolutions from 3 to 10 cm per pixel within a unified multi-resolution pipeline yields a detector ranking first or second across all evaluated datasets. Our dataset, code, and pre-trained weights are made public.

Published as a conference paper at ICLR 2026
SELVABOX: A HIGH-RESOLUTION DATASET FOR TROPI- CAL TREE CROWN DETECTION
Hugo Baudchon 1,2,9, Arthur Ouaknine 33,4, .Martin
Martin Weiss 1.2, Mélésandd Teng
Thomas R. Walla', Antoine Caron-Guay', Christopher Pal-.6, Etienne 2aliberté2.1,
1 Mila Quebec Al Institute Université Montréal McGiil University
"Rubisco AI "Colorado Mesa University
Polytechnique Montreal
Tuugo. wow..bauuccoonuuuttteel....
ABSTRACT
Detecting individual tree crowns in tropical forests is essential to study these complex and crucial ecosystems impacted by human interventions and climate change. However, tropical crowns vary widely in size, structure, and pattern and are largely overlapping and intertwined, requiring advanced remote sensing methods applied high-resolution imagery. Despite growing interest in tropical tree crown detection, annotated datasets remain scarce, hindering robust model development. We introduce SELvABOX, the largest open-access dataset for tropical detection in high-resolution drone imagery. It spans three countries and contains more than 83 000 manually labeled crowns order of magnitude larger than all previous tropical forest datasets combined. Extensive benchmarks SELVA BoX reveal two key findings: higher-resolution inputs consistently boost detection accuracy: and 2 models trained exclusively on SELVABoX achieve competitive zero-shot detection performance on unseen tropical tree crown datasets, matching exceeding competing methods. Furthermore, jointly training SELVABOS Box and three other datasets at resolutions from 3 to 10 cm per pixel within a unified multi- resolution pipeline yields detector ranking first second across all evaluated datasets. Our dataset,' code,"." and pre-trained weights are made public.
crown
Panama Brazit
Ecuador
The SELVA BoX dataset. The illustrated samples extracted from rasters recorded in Panama, Brazil and Ecuador with a spatial extent 80m x 80m and a resolution of 1.2 to 5.1 em per pixel. The red square the right highlights zoom of the Ecuador sample with spatial extent of 40m x 40m at the same resolution.
Figure 1:
INTRODUCTION
Tropical forests cover 10% of the land area, but they store most biomass and biodiversity of plants our planet (Pan et al., 2011; Gatti al., 2022). Large trees that reach the upper canopy have a disproportionate influence on the functioning of tropical forests, with the largest 1% of trees storing
Box dataset:
PPreprocessing library (geodataser)): https: //gtthbb. hww./h/ggabuuucoooogooooaseeeeee Inference, training benchmark (CanopyRS)) https: terithub..ccm http:////......ccoooooo//a/aaaaa

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Published as a conference paper at ICLR 2026
half of the carbon of forests worldwide (Lutz et al., 2018). However, tree demography patterns in tropical forests are being altered, with increasing tree mortality, due to climate change (Brienen et al., 2015; Bonan, 2008; Esquivel-Mueller Muelbert et al., 2019) and human interventions (Harris et al., 2021). As such, monitoring individual trees in tropical forests is essential to understand the current and future potential of these forests to regulate the global climate (Davies et 2021).
Monitoring tropical trees is a difficult task involving slow, costly, and dangerous ground surveys by forest technicians (de Lima et al., 2022). Forest plots of tens of hectares are the gold standard of tropical tree monitoring to measure and map each individual, but completing a single one can take years of dedicated work by large teams of experts (Davies et al., 2021). Remote sensing technologies considerably augment field work, facilitating forest cartography through aerial detection of individual trees across spatial extents vastly exceeding the practical limitations of ground- based inventories (Brandt et al., 2020). Satellite imagery has been used successfully in forest monitoring tasks (Ouaknine et al., 2025), including height map estimation (Tolan et al., 2024; Lang et al., 2023) and individual tree crown detection (Brandt et al., 2020; Tucker et al., 2023; Zheng et al., 2020; 2023; 2025). However, the highest resolution satellite imagery is typically 0.3-0.5 m, which is still too coarse to distinguish trees in dense tropical forest canopies. Moreover, cloudy conditions complicate satellite sensing in the tropics.
By contrast, unoccupied aerial vehicles (UAVs) or drones achieve em-resolution (< 5 cm), albeit at the expense of spatial coverage (Reiersen et al., 2022; Vasquez et al., 2023; Cloutier et al., 2024). Recently, datasets of UAV LiDAR (Puliti et al., 2023; 2025; Gaydon & Roche, 2025) and methods for forest structure assessment with LiDAR data have been extensively developed (Bai et al., 2023; Ma al., 2023; Vermeer al., 2024; Henrich et al., 2024). However, the cost of LiDAR sensing limits its adoption in tropical contexts where researchers are financially disadvantaged (de Lima et al., 2022) and calls for the development of RGB-only methods. Most open access high-quality, high-resolution tree detection RGB datasets represent temperate forests of the global North (Tab. 1). Tropical forests remain severely underrepresented and have relatively modest annotation counts (Ball et al., 2023b; Vasquez et al., 2023) despite their critical significance for biodiversity and carbon storage.
The high tree species diversity (Gatti et al., 2022) and heterogeneity in crown sizes, shapes and textures (Fig. 1 and 2) in tropical forests pose unique challenges. Indeed, solving the problem of detecting numerous objects of highly variable sizes within the same scene is still an open topic in computer vision applied to remote sensing (Rabbi et al., 2020; Li et al., 2021; Bashir & Wang. 2021). While convolutional neural networks (CNNs) remain the predominant approach for individual tree crown detection (Weinstein et al., 2019; Zamboni et al., 2021; Onishi & Ise, 2021; Yu al., 2022; Ball et al., 2023b; Zhao et al., 2023; Bountos et al., 2025; Hajjaji et al., 2025), recent studies have explored transformer-based architectures on satellite imagery (Jiang al., 2025), motivated by their effectiveness in multi-scale object recognition tasks (e.g., Liu et al. (2021); Zhang et al. (2023)). However, comprehensive, resolution-awart benehmark comparing these two paradigms on UAV imagery across diverse forest ecosystems and -f---iiiiritttioon scenarios remains absent. With the growing number of UAV datasets acquired with different flight parameters, models that can generalize across resolutions and standardized frameworks are needed to bridge the gap between the ecology and computer vision communities.
We address these challenges through our contributions: SELVABOX, a high-resolution drone imagery dataset spanning three neotropical countries (Brazil, Ecuador, Panama) and comprising over 83 000 manual bounding box annotations individual tree crowns; 2 An exhaustive benchmark of detection methods at varying resolutions and input sizes, including a standardized evaluation frame- work for UAV rasters and comprehensive assessment of model generalization (OOD) samples; 3 oftte-of-the-arse art models trained for tree crown detection out- out-performing com- peting methods both topical and non-tropical forest datasets, in both in-distribution (ID) and OOD settings; and (4 two open-source Python libraries facilitating raster preprocessing, inference, postprocessing and standardized benchmarking. These contributions aim to simultaneously advance tropical forest monitoring and applications of machine learning to critical environmental challenges.
out-of-distribune
RELATED WORK
High-resolution drone imagery enables detailed tree characterization at the pixel level (see Figure 1). This capability has catalyzed the development of open access forest monitoring
Datasets.
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datasets (Ouaknine et al., 2025) specifically designed for tree crown semantic segmentation tasks, including pixel-wise canopy mapping (Galuszynski et al., 2022), woody invasive species identification (Kattenborn et al., 2019), and tree species classification (Cloutier et al., 2024; Kattenborn et al., 2020). Tree crown semantic segmentation, a pixel-wise classification task, cannot inherently distinguish individual trees, making it unsuitable for applications such as tree counting or biomass es- timation where individual tree crown detection and delineation are essen- tial (Fu et al., 2024). Datasets for in- dividual tree crown detection (Wein- stein et al., 2021; Reiersen et al., 2022) and delincation (Ball et al., 2023b; Firoze et al., 2023; Vasquez et al., 2023; Cloutier al., 2024; Lefebvre & Laliberté, 2024;
Table 1: Related datasets. The number of tree crowns manually" annotated ('# Trees') are noted in 'k' for thou- sands. The reported resolution or ground sampling distance ('GSD") is in centimeter per pixel. We define the forest 'type' as either urban, plantation, natural; 'biome' as either tem- perate, tropical or worldwide (when the dataset spans over several biomes). "except for Refores Tree, see Section 4.
Name
Type nataral
Boome
NeouTreelvall (Weinstein
2021)
temperate
Refores (Reiensen al. 2022)
plantation
topical
Finose af (Finose et al., 2023)
6.5k
2-5
nonural
temperate
Detectnee2 (Ball et al., 20235)
1.8k
nonural
tropical
BC15tta (Vasquez et al. 2023)
4.7k
4.3
nanural
topical
RAMFORESTS (Troles et sl. 2034)
27k
1.6-1
1.8
noteral
tomperate
QucheeiTios (Clonties ct ci., 2024)
23k
1.9
cstural
lamperate
et al., 2024), corresponding object detection and instance segmentation tasks respectively, have been proposed for both general forest monitoring and specialized upplications such as dead tree identification (Mosig et al., 2024). Table summarizes open access datasets for general tree crown monitoring Despite considerable community efforts to share manually annotated tree crown data, substantial gap remains in datasets for monitoring tropical trees in natural forests.
Quebee Plancatinn (Lefeebrre & Laiibenté 2024)
19.6k
0.5
planttioon
temperate
OAM CAMM-TD (Netchh Michaells ct al. 20224)
280k
10
mosily urban
worddwae
SELVAHOX (ours)
M5K
1.2-511
natural
tropical
Modeling. Deep learning is the dominant paradigm for individual tree crown delineation, supersed- ing earlier computer vision and machine learning methods (Kattenborn et al., 2021). Open access datasets (Tab. 1) have facilitated the development of individual tree crown detection models with deep learning architectures, including Faster R-CNN (Ren et al., 2015b), Mask R-CNN (He et al., 2017), and RetinaNet (Lin et al., 2017), as demonstrated with DeepForest (Weinstein et al., 2019) and Detectree2 (Ball et al., 2023b). These CNN-based methods have proven effective in diverse forest scenarios (Zhao et al., 2023). Tree crown models have also leveraged SAM (Kirillov al., 2023) by providing efficient prompts for zero-shot tree crown delineation (Teng et al., 2025). While the FoMo benchmark (Bountos et al., 2025) has explored transformer- based architectures including pretrained DeiT (Touvron et al., 2021) and DINOv2 (Oquab et al., 2024) backbones, advanced transformer- based object detection methods (Liu et al., 2022; Zhang et al., 2023) remain underexplored in this domain.
Evaluation. Previous open access datasets (Tab. 1) have evaluated detection methods using classification-based metrics per tree (recall, precision, Fl-score) (Weinstein et al., 2020; 2021: Zheng et al., 2021; Beloiu et al., 2023) with detection-based metrics such as intersection over union (IoU) and mean average precision (mAP) (Hao et al., 2021; Yu et al., 2022; Ball et al., 2023b; Fu et al., 2024; Firoze et al., 2023; Veitch-Michaelis et al., 2024; Bountos et al., 2025). UAV rasters usually divided in tiles for training and evaluation, but tile- metrics are susceptible to edge effects (where partial trees appear at tile boundaries) and duplicate detections when scaled to larger areas, complicating accurate tree counting. As a consequence, tile-level metrics fail to accurately represent performances at the entire raster level, which is what matters practioners. For example, tracking the mortality of large tropical trees over time and across vast areas requires aggregating detections from individual tiles into a coherent raster-level map. A recall metric for keypoint-in-tre prediction tasks at the raster level was proposed to evaluate OAM-TCD (Veitch-Michaelis et al., 2024). In this work, we extend the evaluation of aggregated predictions from individual images to detection- tasks, including both precision and recall metrics (F1-score) as well as the location of each object.
Multi-resolution. Despite growing interest in multi-scale and multirresolution analysis for deep learning in remote sensing applications (Reed et al., 2023; Bountos et al., 2025), these approaches remain understudied for forest monitoring. While increased spatial extent per tile improves tree crown classification (Näsi et al., 2015; Liu et al., 2020; Kattenborn et al., 2020) and higher tile resolution benefits tree crown semantic segmentation more than increased spatial extent (Schiefer al., 2020), esolution-induced domain shift remains challenging for individual tree crown detection.
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Current pre-trale pre-trained models (e.g., DeepForest, Detectree2) show poor zero-shot performance OOD samples (Gan et al., 2023), though targeted fine-tuning can mitigate this gap (Bountos et al., 2025). Further research is needed to evaluate how tile spatial extent, size, and resolution impact detection performance and to develop fine-tuning methodologies that reduce zero-shot degradation on OOI) samples, particularly given the substantial size variation in tropical tree crowns (Fig. 2).
3 THE SELVABOX DATASET
FORU
020
SelvaBox dataset Brazi ZF2 Equador Tipulini
We present SELVABOX, large-scale benchmark dataset addressing the critical open-access annotation scarcity in tropical forest remote sensing (Sec. 2) while mo- tivating research in individual tree crown detection. SELVABOX encompasses 83 137
Number objects scale)
Panama Agua Salud
1000
100
12 15
individual tree crown bounding boxes on top of 14 RGB orthomosaics, including 96.6 ha in Brazil, 96 ha in Panama and 318.1 ha in Ecuador, recorded with four different drones (DJI Mavic Entreprise [m3e], DJI Mavic Multispectral [m3m]. DJI Mavic Pro [mavicpro], DJI Mavic Mini 2 [mini2]) at ground sampling distance (GSD) between 1.2-5.1 cm per pixel (Tab. 6 in App. A.1). Our drone imagery was acquired over primary and secondary forests, and native tree plantations. It includes diverse sets and shapes of tropical trees as depicted in Figure 1. More details about the orthomosaics can be found in App. A.I.
22
2-5
5-10
10-20
20-30
30-40
40-50
>50
Box side length (m)
Figure 2: Distribution of box annotations size in SELVABOX per country.
Locations. The RGB imagery was acquired in three countries: Brazil, Ecuador, and Panama (Tab. App. A. The Brazil data was collected at the ZF-2 station, a forest with high-diversity characteristic of the Central Amazon and growing on nutrient-poor soils. The topography consists of platcaus dissected by valleys (Amaral et al., 2019). The Ecuador data was recorded at the Tiputini Biodiversity Station (TBS), located within the Yasuní Biosphere Reserve, one of the most biodiverse forests on Earth (Valencia et al., 2004). The climate of this Western Amazonia region is considered to be aseasonal compared to Central Amazonia while the soils tend to be richer in nutrients as they derived from younger sediments from the Andes (Hoorn et al., 2010). Finally, the Panama data was acquired from four areas of the Agua Salud Project (Mayoral et al., 2017). Two areas are plantations of native tree species (Mayoral et al., 2017), while the other two are from surrounding secondary forests. The soils of Agua Salud are acidic and nutrient-poor (van Breugel et al., 2019). The tree species diversity of Central Panama is considered lower than our other two Amazonian sites.
Annotations. The data was manually annotated by five trained biologists. They were asked to draw bounding boxes around every individual tree crown they could reliably detect from the imagery. They generated 83 137 manual tree annotations during 1 284 people-hours with crowns spanning from < m to > 50 m in diameter (Fig. 2). All annotations were produced with AreGIS Pro version 3.0, stored in hosted feature layers AreGIS Online, have georeferenced coordinates, and were exported to geopackages. Figure 2 shows the tree crown annotation bounding-bee box size distribution, where we notice a long-tail distribution for larger trees, especially in Ecuador.
Our annotation process used photo-interpretation, the most reliable and feasible approach at this scale. Field-based validation faces significant technical constraints in tropical forests, including GNSS signal blockage, multipath errors from dense canopy, difficulties linking non-straight tree trunks to canopy imagery, and variable geolocation errors making the process less efficient and accurate than intopriiterrpetttiionn (Laliberté et al., 2025). Additionally, logistical challenges like intense heat, humidity, heavy rain, and dense vegetation make fieldwork costly and hazardous. Since LiDAR data requires expensive equipment and specialized annotators compared to photo-interpretation. it is less accessible for tropical forest scientists, who have limited research funding (de Lima et al., 2022). Consequently, we adopted an RGB-only validation approach to ensure scalability and broad applicability. The annotation process followed standardized protocol with initial training of domain- expert annotators, multi-pass annotation reviews, and systematic quality control (see App. A.3 for
errors-making
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full details). We supplemented visual interpretation with digital surface models (DSMs) derived from 3D photogrammetrie point clouds, using elevation data to distinguish adjacent crowns with similar visual features but different heights.
Spatially separated splits. We propose train, validation and test splits, created spatially in the rasters to ensure no pixel overlap between splits and avoid geospatial auto-correlation (Kattenborn et al., 2022), and including 61. .k, 9.6k, and 10.6k boxes respectively. We define our splits by manually creating areas of interest (AQIs) geopackages in the QGIS software (Fig. 5 in App. A.2). Orthomosaic borders with poor visual quality were deliberately excluded during AOI creation to ensure clean, artifact-free splits. For the test split, we defined the AOIs on rasters with minimal visual reconstruction artifacts while including maximal diversity and quality in box annotations.
Incomplete annotations. Although considerable effort was put into producing a dense tree-crown mapping during the annotation process, some annotators reported difficulties clearly distinguishing a subset of individual trees on one raster in Brazil and three rasters in Ecuador, resulting in sparser annotations. Annotation sparsity is common challenge in tree detection datasets: The Detectree2 dataset contains only tiles covered in area by at least 40% tree crown annotation polygons (Ball et al., 2023b). This method introduces noise during the training process as annotations may be missing for up to half of the trees in an image, causing misleading penalization. We adopt a different strategy where we mask targeted pixels in AOIs with missing annotations when dividing the rasters in tiles. During training, we expect models to become agmostic to such masked pixels, i.e. not predicting boxes in those areas, thus not being penalized due to missing annotations. Such holes were created for train AOIs, sub-set of valid AOIs, while test AOIs were chosen to cover areas where annotations are dense and complete. Figure (in App. A.4) shows an example of pixels masked that way.
Tiling and preprocessing. When tiling the rasters, i.e. dividing rasters into tiles, geopackages to mask pixels that are outside of each tilc's assigned split. Each tree crown annotation assigned single split where it overlaps the most according to the AOIs. For each tile, we keep annotations that overlap at least at 40% with the tile's extent. For the ready-to-train dataset, we remove tiles that contain no annotations, more than 80% black (masked), white or transparent pixels. A sliding-window window tiling approach was used, with 50% tile overlap for the training and validation splits, and 75% for the test split to ensure that the largest trees entirely fit in at least one tile (Sec. 4). We release our preprocessing pipeline as python library called geodataset. The final preprocessed dataset is available on HuggingFace under the permissive CC-BY-4.0 license.
AOI
4 BENCHMARKING MODELS AND METHODS
We structure our experiments in three sequential phases. First, we identify effective modeling choices by evaluating various object detection models and input image settings on SELVABOX, examining how resolution and spatial extent influence detection accuracy based on in in-distribution performance (Sec. 4.1). Second, we validate the efficacy of multi-resolltion domain augmentation by testing whether multi-resolution training improves or degrades performance compared to single-resolution training (Sec. 1).. Finally, we assess generalization to other datasets by evaluating three categories: models trained exclusively on SELVABOX, models trained on SELVABOX combined with additional datasets, and models trained without SELVABOX including external methods (Sec. 4.2).
In addition to BOX, we use the OAM-TCD (Veitch-Michaelis al., 2024). (Weinstein et al., 2022; 2021), QuebecTTees Trees (Cloutier et al., 2023; 2024), BC150ha (Vasquez et al., 2023), and Detectree2 (Ball al.. 2023a) datasets. We excluded the Quebec Plantations dataset (Lefebvre & Laliberté, 2024), as it comprises non-tropical, young tree plantations outside the scope of our study. Similarly, we excluded ReforesTree (Reiersen et al., 2022), a tropical plantation dataset whose bounding box annotations were generated by inference from a fine-tuned DeepForest model (Weinstein et al., 2020), resulting in noisy annotations unsuitable for robust training or evaluation (Fig. 13 in App. F.4). Additionally, we omitted the dataset published by Firoze et al. (Firoze et al., 2023), as it was designed for image sequence-bart based tree detection, with annotations derived from highly overlapping, video-like like image sequences, introducing redundancy and requiring extensive preprocessing. Given that each dataset varies in ground sampling distance (GSD), tree crown size distribution, annotation type, and predefined splits areas of interest (AOIs), applied independent
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preprocessing procedures detailed in Appendix F.1. Our benchmarking, inference, and training pipelines are publicly available in our Python repository CanopyRS.
Evaluation metrics. To evaluate models at the tile level, we consider the industry-standart COCO-style mAP50: and mAR50:95 metrics (Lin et al., 2014). Due to the high number of ob- jects per tile in SELVABOX (at 80m ground extent, see Sec. 4.1), QuebecTrees and BCI50ha, we increase the maxDets parameter of COCOEval from 100 to 400 for those datasets.
As detailed in Section 2, tile-level metrics do not necessarily reflect raster-level performance, which is the operational target for concrete upplications such as large-scalt forest inventories. To address this, we propose RF1 a Raster-level Fl score evaluating final predictions after tile aggregation via Non- Mon-Maxiuum Suppression (NMS). It uses the same greedy matching as COCO metrics, but requires a single, strict IoU threshold of 2 75% for a match. This 75% threshold is a balanced criterion for dense canopies, where 50% IoU is too permissive and 90% is overly difficult. By integrating the F1 score at the raster level with this IoU restriction, the RF1 metric accounts for precision and recall, both important in forest monitoring applications. For each dataset, we tune NMS hyperparameters on the validation set, apply the optimal settings to the test set, and report the final RF1 as a weighted average over all rasters (details in App. B.3). While annotation noise makes perfect score of 1.0 unlikely, maximizing RF175 is practical target for reliable ecological monitoring.
Model architectures and training. We compare four object detection approaches for tree crown delineation: 1 Faster R-CNN with ResNet-50 backbone (Ren al., 2015a: He et al., 2016), widely used CNN-based detector; 2 DeepForest (Weinstein et al., 2019; 2020), a RetinaNet variant Detectree2 (Ball et al., 2023b), a Mask R-CNN trained on dataset also called Detectree2, evaluated in two variants: 'resize" rnulti-resolutton tropical) and 'flexi' (joint tropical- -ppical--rban urban training); and 4 DINO (Zhang et al., 2023), a DETR- transformer model that we evaluate with both ResNet-50 and Swin-l. backbones (Liu et al., 2021). Note that DINO (the DETR-based detector) and DINO (the self-supervised embedding model) are unrelated despite sharing the same name. While recent DETR-based architectures have reached similar or better performances (Zong ct al., 2023), we chose DINO for its adoption by the community through Detectron2 (Wu et al., 2019) and Detrex (Ren al., 2023). DINO, Faster R-CNN, DeepForest, and Detectree2 serve as strong and diverse baselines from both general-purpose and domain-specinf tree crown detection literature (Sec. 2). All models are initialized from COCO- COCO- pretrained checkpoints. We implemented our own augmentation pipeline, and use standard crop, resize, flip, rotation and color augmentations (App. 1) Training sessions took between 12 hours and 3 days for both architectures. All hyperparameters used for training and testing are detailed in Appendix B.2.
trained on Neon Nreaareeenvluuatin::
4.1 MODEL, RESOLUTION AND SPATIAL EXTENT SELECTION ON SELVABOX
We choose raster tiling scheme that balances detection accuracy, object coverage, and hardware constraints. Our standard tile is 80 x 80 m at 4.5 cm/px (1777 x 1777 pixels). This setting ensures that the largest crowns in SELVABOX, some upwards of 50 m in diameter (Fig. 2), fit entirely within one tile when using 75% overlap between tiles in our test set, while keeping our models (e.g. DINO 5-scale with Swin-L) trainable on 48 GB GPUs with a batch size of one per GPU.
To assess the trade-offs between spatial resolution and ground extent, we conduct an ablation study across three configurations (Sec. and Tab. 3). We vary the resolution between 4.5, 6, and 10 cm/px, yielding 1777 x 1777, 1333 x 1333, and 800 x 800 pixel inputs for a fixed 80 x 80 m ground extent. In parallel, we test 40 x 40 m tiles, which contain fewer crowns per image and still guarantee that over 99. 99.9% of crowns- those smaller than 30 m-are fully visible in at least one tile, assuming 75% overlap. This ablation isolates the effects of spatial detail, object count, and input size. Fach model is trained at a fixed resolution, with only minor cropping augmentation (+-00% of input size) before resizing to a fixed input size. Further experimental details are provided in App. C.
We also compare models trained at 6 cm and em GSD while resizing the inputs to assess the impact of both the resolution and input size on models performance. Tile-level evaluation metrics (mAPa0.95 and mAR50:95) are not comparable per between 40 x 40 and 80 80 m spatial extent since the tiles differ in object count and spatial boundaries. But one may compare all results with the RF175 since it is computed at the raster level, after aggregation of individual images predictions.

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Tables 3 and 4: Model, resolution and spatial extent selection on SELVABOX. Comparison of performances on the proposed test set of SELVABOX with variable tile spatial extent, respectively 40 x 40 m in Tab. 2 and 80 x 80m in Tab. 3, input size in pixels and ground spatial distance (GSD) in cm. We highlight results per method and backbone as the first, best scores. We also bold and underline the best and second best scores overall. Note that mAP30:93 and 50 cannot be compared between 40 x 40 m and 80 x 80 m inputs as images do not match, but we can use RF175 to compare final post-aggregation results at the raster-level.
the second and the third
Table 2: SELVABOX at 40 x 40 m.
Table 3: SELVABOX at 80 x 80 m.
Mathod
CSD
size
MAP
RF1
Methad
GSD
size
mAPaa
mARoue
RF
10
500
c1190
40.87 :200300
35.78
c20 40
10
800
24.94 (20.44)
35.93 (28-55)
54.66 -0000)
ResNet50
R-CNN
Faster
10
10
958
6666
888
666
29. 10
28.51 20
28. 40: 120
29.3 31 das
43.59
42.77
44.18
43.36 (00.00
:2:0 100
:.0.10
57.46
57.75
59.97
38,92
(10 91
(60 200
ResNe150
Faster CNN
10
10
6
1333
1777
1335
26.52
27,
23.89
25.25 c2m.34
(40.2))
41.02
39.55
40.21
38.59
35.74
36.22
36.09
$5.94:1
858
30:25
45.18
4.5
30.63 220
48.06
29 (:0033
ResNNe55
4-acale
DINO
10
5665
888
6166
888
32.19 (10 300
53.5 c2.54
31.76
33 46 c2o ets
50.40
50.68
$1
52.
80
(.0.50)
:1:0 203
12.0.10
44:55
41.57 461.90
43.34
42.47 (20 o7)
(29 79+
(50 186
ResNet50
4-scale
DINO
10
10
6
6
1277
1777
1333
32,51 080.500
33.62 23::30
33.06
c2 54g
0.0000
$0.85
40:93
49.35
(
42. (40.00
42 29 (41.20)
64.18 (591380
4.5
85S
34.
130
52:53
(600,00
44.26 (1. RD
4,5
1777
33.81
(68.54)
$1.00
43. (10.40)
10
400
33
52.02 (201200
45.37
10
800
33.90 (50.00
50.29
64.64 $00.50
Swin 1-584
5-scule
DINO
B88
666
f166
888
34.92
34.64
36
(an 1o 37707
c23 280
52.91
53.23 :240140
54.35
22m cm
46.39
48.13 13 on
S-cale
DIND
384
10
10
6
(46.54
50.76
52.91 (e6-90
$2.12
53.56 1::444
45
4.5
888
37.78
17788-0..22 -..35
56.30
(m0.21)
49.76 :-0.59
4.5
1777
37.79 22.008
54.66
(330.4))
49.38 (g1-77)
Multi-resolution approach. Diversity in camera sensors and recording conditions results in datasets with various resolutions (Tab. I and 6), complicating or preventing model training across multiple datasets. We mitigate this through multi-resolution input augmentation that enforces scale-invariance during training, enabling to combine datasets of various resolutions. This simple, yet efficient process randomly crops inputs using a wide range of crop sizes, then randomly resizes the crops. This achieves two effects: cropping performs ground extent augmentation, and 2 resizing performs GSD augmentation. Details on our multi-resolution augmentation pipeline are in App. D.I.
While data augmentation generally improves generalization, extreme transformations may impact convergence and performance. Therefore, we train multi-resolution models on SELVABOX with increasingly large crop ranges (Fig. 3) and the same random resize in the [1024, 1777] pixel range, comparing them at 80 x 80 m to the best single-resolution, single-input-side models from the previous experiment (i.e. DINO Swin-384 at 4.5, 6 and 10, see Tab. 3).
4.2 METHODOLOGG TO EVALUATE OOD GENERALIZATION
To evaluate the generalization capabilities of models trained on SELVABOX, we define BC150ha and Detectree2 (Tab. 1) as OOD datasets for test-only evaluation. We perform zero-shot evaluations on these datasets, meaning models are tested without any fine-tuning data completely excluded from training, and characterized by diverse resolutions, image quality, and forest types. These two datasets are considered OOD relative SELVABOX because BC150ha is located on an island in Panama (whereas SELVABOX is on mainland Panama), and Detectree2 is located in Malaysia, on a different continent; and 2 both datasets were acquired using different drones, camera sensors, and flight conditions. Additionally. we include NeonTreeEvaluatinn either in nn-distribution or OOD datasets assess how varying the number and diversity of datasets used during training affects model generalization.
QuebecTrees, and OAM-TCD as
We compare a multi-resolltion model trained exclusively on SELVABOX, using a crop augmentation range of [30, 120] meters (equivalent to [666, 2666] pixels), against models trained on different combinations of OAM-TCD, NevvataeeEallution, QuebeeTrees, and SELVABOX datasets (including Deepforest and Detectree2). We selected this multi-resolution augmentation range based our benchmark results (Sec. 5, Fig. 3), which indicated that this range achieves performance comparable to resolutin and less aggressive multi-resolution methods on SELVABoX, while also allowing spatial extents of images from different datasets to partially overlap (Tab. 19 in App. F.I). Finally, we optimize non-maximum suppression (NMS) hyperparameters using the validation sets of BEx and Detectree2, while keeping BC150ha strictly zero-shot.
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5 EXPERIMENTS AND RESULTS
50
45
First, we evaluate model architectures, resolutions, and spatial extents on SelvaBox (Sec. 5.1). Then, we validate our multi-resolution training methodology. Finally, we assess generalization on OOD datasets (Sec. 5.2).
RF1ys Score
-0 single-res. 4.5cm
-8 single res. 6cm
-6
single-res. 10cm
multi-rs [36, 88]
5.1 SELVABOX RESULTS
mult res [30, 100)
multi-rss 120]
Using the methodology in Section 4.1, we find:
10
Ground Sampling Distance (cm/px)
Resolution matters, transformers too. In Tables 2 and 3, we find that for all GSD and spatial extents, DINO outperforms Faster R- R-CNN, and Swin L-384 outperforms ResNet-50. We also observe significant improvements in mAP 55550: and RF175 when using lower GSD for all architectures. While larger input sizes at fixed resolution benefits ResNet-50-basse Swin L-384 models do not see such improvements at 6 cm per pixel. This suggests diminishing returns from further increases in input size, and only the Swin L-384 backbone fully leverages more detailed inputs. Finally, we observe that Faster R-CNN reaches best RF175 performance 40 x 40 m rather than 80 X 80 m, likely due to larger context and higher number of objects making the task more difficult.
Figure 3: Multi-resolution single- resolution on SELVABOX. RF1 for the best single-resolution methods from Tab. 3 trained at fixed 80 x 80 m extent vs multi-resolution approaches with vary- ing crop augmentation ranges [36, 88). [30, 100), [30, 120). All methods are "DINO 5-scale Swin L-384".
methods, DINO
Multi-resolution effective on SELVABOX. In Figure 3, we observe that all multi-resolution models achieve RF175 results within three resolutions. Additionally, single-resolution models struggle at test-time on unseen resolutions. Results for mAP30:95 and mAR50:95 similar and presented in Appendix (Fig. 8). This demonstrates that a single multi-resolltion model can be trained for transferability across spatial extents and GSDs without performance losses on SELVABOX, instead of training multiple resolution-specific models.
of the best single-resolution models, for all
5.2 OOD RESULTS
Following the methodology described in Sec. 4.2, we evaluate zero-shot generalization, we find:
SELVABOX exposes the limitations of current methods and datasets. We report results on tropical forests in Table 4. First, existing methods, namely Detectree2 and DeepForest, perform poorly on SELVABOX in zero-shot evaluation with 6.08 and 13.14 RF1 respectively. Our method trained Neon TreEEuauati,,,, QuebecTrees and OAM-TCD reaches 30.81 RFI
with multi-resolutions on SELVABoX in zero-shot evaluation, showing great generalization performances on unseen tropical forests. When SELVABoX is included in-distribution of the training process, our methods achieve state-of-the-art performances with 47.6 6.63 (multi datase-s SELVABOX) and 48.60 (SELVA Box only) RF1 These experimental results show how challenging SELVABOX is for existing methods, filling gap not covered by existing datasets and methods.
SELVABoX improves OOD generalization on tropical datasets. We observe that models trained on SELVABOX achieve state-of-the-art performance in zero-shot evaluation on BC150ha, at 39.39 (multi-datasets + (BLVABox) and 11.91 (SELVABOX only) RF1 followed by Detectree2-resize at 34.97 RF1 On the Detectree2 dataset, the best performing model is Detectree2-resize in RF1 although a potential data leak could have occurred during the evaluation on their dataset, which limits the interpretation of the results, given that we were unable to recover the training-test splits originally used. Our multi-datased
SELVA Box method outperforms both Detectree2's models in terms mAPs0:95 and mAR50.95 on the Detectree2 dataset and beats DeepForest. also outperforms our multi-dataset without SELVABOX and SELVABOX-OWe methods, while being evaluated on restricted zero-shot regime. We include corresponding qualitative results in Appendix F.5. To our knowledge, the DINO-Swing trained multi-dataset + SELVABOX including multi-resolltion training process achieves ofate-of-the-as art performance for the tropical tree crown detection task, generalizing well on both SELVA Box and OOD tropical datasets.
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Table 4: Tropical datasets evaluation. We respectively denote N for Novauueeevaluation, D for Detectree2, D+u for Detectree2 with urban regions. Q for QuebecTrees, O for OAM-TCD, S for SELVABOX and B for BC150ha. We noted OD to identify out-of-distribun relative gain in RFI 75 of each method compared to the best competing one (Detectree2-rezise in this table). We mark the best and second-best scores in bold and underline, respectively. We denote with ~ the Detectree2 competing methods where original train-test splits could not be recovered, preventing controlled evaluation on their dataset and limiting the interpretability of comparative results. Standard deviations (over three seeds) are reported only for models we trained ourselves, whereas Detectree2 and DoepForest (N) rely on single released model without variability estimates.
datasets, and RG for
Method
Train set()
SELVABoX (S)
Derectree2 (D)
BCLo0ha (B)
mAP
RG
mAAPrsss
mARResss
RF1ss
ODRG
mAP
mA
KP
RG
Detectree-resize
15.47
0%
17.67
14.11
2357
0%
32.11
48.18
34.97
0%
Detoctme2-llxii
Des
6.43
13.20
9.21
-00s
5.41
19:86
4,46
4079
12.72
20.47
4.26
DespForest
4.70
900
6.08
-488
6,85
19.27
7:83
V 48%
14.48
25.50
10.02
-22
R-CNN-RN50
N
1.29:2031)
11
11.08
11.00 on 26
1480
/ -382
0.72600
4.4720009
142ian 10
DINN--Swit-L
5.67:00 17.4%a: 1n
9.4 ito / .258
14.77caa.00
14.7
32
32.62 m
19,87.44
/n
1.74/80832
11
11.80g9:50
3.77inn
.006
Decpforest
28,84:10.31
48,67aamm
38.00:n222
X 1189%5
6.34
18.351117
2.7caa / 499
25.17183.00
46.
46.85--977
36.46111300
14%
R-CNN-RNS0
28 33.49 sn.
41.88
4
117695
8.32
1.04:000
27.23641.46
31 34 at
-115
DIND-SWinLL
37.77 2.8.88 (1..0.555544...00 54.69
45.60
+.9%
13,27 1.00
28
V .55%
36.87150.00
60.30.59.00
10.00
41.91: 1:2.
-19%
DeepFurest
NOO
24.93 31,
um
21. 31
-645
8.19 /f 6615
10.84
31.13
13.58a1
.215
R-UNN-RNSO
NOO
16,39
29.39
29.
24.77:40.50
-85%
12.50
28.17
13.05 of 445%
11.92/48.43)
32.74m4200
16.16aaa
.549
DINO-Swir-I.
NQO
20,85:a1440
15.35
30.51
11.31aa of -594
25.72ta1 48.
18.78u1.
32
DeepFureet
NOOS
43
35.92:a1.00
+173%
12.77
43.50ua..1
-296
P.R.CNA CNN RN30
NQOS
24.93
39.3460.30
30.50:11444
+1.2%
13.90u1.00
29.341127
14.42
405
20.42
43.25uu1.50)
23.49ta1.rs
33%
DIND-SWai-L
NQOS 5695540.4
53.7 47.63:44
-360%
g8.20 35.20) 19.23 v -200
33.13
58.366-2.00
39 Way +129
Table 5: Non-tropical datasets evaluation. We respectively denote N for NeonTreeEvaluatiin, D for Detectree2, D+u for Detectree2 with urban regions, Q for QuebecTTrees, O for OAM-TCD, for SELVABOX and B for BC150ha. We noted OD to identify out-of -isti--distiibution datasets, and RG for relative gain in RF175 if available, mAP50:95 otherwise, of each method compared to the best competing one (either DeepForest (N) or Detectree2-flexi in this table). We mark the best and second- best scores in bold and underline, respectively. We cannot compute RF175 for Neon Evaluuatttionn and OAM-TCD only individual images are available for their test splits. Standard deviations (over three seeds) are reported only for models we trained ourselves, whereas Detectree2 and DeepForest (N) rely on single released model without variability estimates.
Train set(s]
Noon Eevaltiovvotttiion (N)
Qucho.Troes (Q)
OAM-TCD (O)
Method
mAPsess
mARsaso
RFD,, RG
mAPsons
mARsuss
RF1ss
OD
mAPanas
mARsoss
RFD,OO
Detesinee-resiie
4.09
15.67
N/A
-28%
7.62
13,85
13.98
-11%
2,45
12.43
NIA
-51%
Detectre2 Alosi
Dec
1.75
9.86
N/A
91%
9.75
16.59
15.00
0%
5.20
13:21
NIA
-16%
DeepForest
N
18.06
25.82
NAA
X
0%
3.58
7.32
4.82
/
.7%
6.19
11.42
NIA
08
F.R R-CKN RN50
17.08:a0.11
27.16:50.0
6%
5,97:39.4
18
10.66:10.0
V 32%
9.75:50.21
18.852000
+57%
DINO-Swin-L
23.68:a2.20
NAA
10.46:2220:
23.47
14,20
9%
18.42:11m
29.91:11.0
+197%
DeupForest
1.6::003
5.50:
N/A
94%
21.46:2u.4m
36.29
29 31. 10 Vf +99%
9.68:
21.95:21.0
NIA
+56%
R-CNN--RNUI
0.63
2.9%
am
NIA
97%
17.65c59
271
30.71cau
om
26 10 son of -60%%
8.50 801
16.17:21
N/A
-37%
DINO-Swin L
5.16
14.87:a1.47:
NAA
72%
27.34
44.04cs2
om 38, 64.34 4n V +1459
22.58
35.59)20.52)
NIA
1264%
DespForest
NQO
20.50:a3.00
31.13:a0.1::
1358
36.75
49.66can
47. 47.37 X x2039)
39.00
49.78:50.18)
+530%
R--CNN-RN50
NQO
17.9
26.04c20.0e:
NAA
1%
33.45:20:44
45.68
+179%
38.34/20.29 47.76-303u)
+519%
DINO-Swin-L
NQO
23.50:aa.10
34.85 est
N/A
50%6
44.53
58.48
m0 56.53 eei X +2621%
44.29 soi
55.57:20.44
NIA
+615%
DespForest
NQOS 20.7 20.71: 32, Latto.
NIA
14%
36.53ca9.0n
as: 49 66666- sa1 47. 00 X -2015
38.37ca0.an
49
20:20
NIA
X +519%
R--CNN-RN5U NOOS
18-47
10 28 80:
N/A
+2%
31 9..98.
45 10.30
42 06
X - 1199%
38.08
47 77:87a0
NAA
+515%
DINO Swin
23.00...0++0..4 35.53 +0.50
43,05
$8.74
56. 36.41100
=2619
44.03ka0.33
55.34ca0m
State-of-the-art performance on both tropical and non-tropical datasets. We present results on temperate and urban forests in Table 5. We observe that both our multi-dataset methods (with and without SELVABox) outperforms all the other in-distribution or OOD methods on temperate EveoaureeEvaluation and QuebecTrees) and urban (OAM-TCD) datasets.
Furthermore, training on SELVABOX alone allows our methods to outperform competing approaches on both the MuebecTTress and OAM-TCD datasets, achieving better results on QuebecTree Trees than models trained on NeonTree (a global-scale temperate forest dataset), demonstrating SELVABOX's s quality and the generalization capacity of our training process. We include corresponding qualitative results in Appendix F.6. Our multi-dataset methods reached average performance within standard deviation for non-tropical datasets, confirming that our multi-dataset approach with SELVABOX reaches state-of the-ra performance on both tropical and non non-tropical datasets.

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5.3 ABLATION OF RFI Vs IOU
70
RF175
DNN--AA (S)
60
Delectree waice
Detectred2 Feel
To better understand how the RFI metric varies across IoU thresholds other than 0.75, plotted RFI as a function of the IoU thresh- old (0.50 to 0.95) for SELVABOX (Fig. 4) as well as BCI50ha, Detectree2 and QuebecTrees (App. E). NMS hyperparameters were optimized independently on the validation set indepen- dently for each IoU threshold. Results on SELVABOX are consistent with Tables 4 and 5: we observe a consistent, substantial gap be- tween our in-distribution DINO-Swe Swin-L vari- ants and competing methods (DeepForest and both Detectree2 methods) across all loU thresh- olds. On BC150ha and Detectree2 datasets, the Detectree2-resize baseline exhibits a local performance peak around IoU 0.70, some- times unexpectedly exceeding its scores at lower thresholds (0.50-0.c) 0.65), which we attribute to per-threshold NMS tuning and the higher variance induced by the substantially smaller size of these datasets. This behavior further underlines the benefits of SelvaBox's 's scale, where RFI is less sensitive to annotation noise and crown size distribution. As a natural extension, we leave for future work the design of an RFL RF150:95 metric, analogous to mAP50:95.. in which NMS hyperparameters would be tuned against the average RFI over multiple IoU thresholds.
Deept Grest thi
40
30
20-
50
55
75
8D
RF1 IoU threshold
Figure 4: RF1 vs IoU threshold on SELVABOX. Comparison of two of our DINO-Swin-L variants and competing methods at different ToU thresholds. In this work we focus on RF1 (IoU 75). For each IoU threshold, NMS hyperparameters are indepen- dently optimized on the validation set. Results for other datasets are in Appendix E.
5.4 PRACTICAL ADVICE
We recommend DINO-Swin-L. (NQOS) as the default model for most applications and forest types, as it ranks first or a close second on all datasets. The exception is high-resolution tropical drone imagery without water or human constructions, where DINO- S (S) is preferable. We also recommend tuning NMS hyperparameters, tile extent and ground resolution on a validation set (if available), especially if the trees of interest are either small very large.
6 ETHICAL CONSIDERATIONS AND RESPONSIBLE USE
SELVABOX and the released models are intended to support ecological research and operational forest monitoring (e.g.. biodiversity assessment, biomass and carbon-stock estimation), not to fa- cilitate activities such as illegal logging, land grabbing, or other forms of environmentally harmful exploitation, nor actions that could undermine the rights and livelihoods of local and Indigenous com- munities. We release SELVABOX under CC-BY 4.0 license and code and model checkpoints under an Apa license, both permissive licenses. Although annotations were produced and reviewed by expert biologists, the dataset and resulting models inevitably contain noise and biases, and metrics such as RF1 remain sensitive annotation completeness and evaluation settings. Outputs from 'ELvABox-traing models should therefore be treated as decision-support tools rather than definitive measurements, and not used in isolation for high-stakes management or policy decisions.
CONCLUSION
We present SELVABOX, the largest tropical tree crown detection dataset to date, with over 83, 000 expert-verified annotations from high-resolution UAV imagery across Central and South American forests. We achieve sate-of-the-arr performance across in-distribution and ofitt--disttibuttin bench- marks in zero-shot setting training on SELVABOX and other open-access datasets. We advocate for the RF175 metric, raster-level score reflecting forest monitoring needs, and suggest that future work explore an loU-averaged RF1 metric, as well as alternative aggregation methods such as soft-NMS (Bodla et al., 2017) weighted boxes fusion (Solovyev et al., 2021). Our dataset, code, and models are fully open to support research in forest monitoring, while acknowledging the potential risks of misuse for illegal exploitation.
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REPRODUCIBILITY STATEMENT
All code, data, and experimental details required to reproduce the results of this paper are made available. The SELVABOX dataset is described in Sections and with additional details on orthomosaics, splits and annotations in App. A. The ML-ready SELVABOX dataset is available on HuggingFace and linked on the first page of this manuseript. The raster-level annotations and AOIs in geopackage format are available on HuggingFace in a separate branch. Preprocessing steps for external datasets benchmarked in this manuscript are described in App. F.I, and we also release these preprocessed versions on HuggingFace. Our open-access data preprocessing package, geodataset, and our benchmark, inference and training GitHub repository, CanopyRS, are described in App. G and linked on the first page of this manuscript. The main training hyperparameters and compute setup are described in App. B.2. The RF175 metric pseudo-code implementation and related inference nyperparameters used in our benchmarks be found in App. B.3. Finally, model weights of our best methods as well as smaller model variants are available on HuggingFace and CanopyRS package.
ACKNOWLEDGMENTS
This project was undertaken thanks to funding from IVADO, including the PRF3 project 'AI, bio- diversity, and Climate Change', the Canada First Research Excellence Fund, the Canada Research Chair and Discovery Grant from NSERC to EL., and funding from the Mitacs institute. We thank the many people who helped with the acquisition of data (drone imagery and labels), notably: Sabrina Demers-Thibeauld Vincent Le Falher, Marie-Jeanne Maxime Frégeau, Frédérik Senez, Gonzalo Rivas-Torres, the Outreach Robotics team (especially Hugues Lavigne and Julien Rachiele-Tremblay) Tremblay), Paulo Sérgio, Adriana Simonetti Peixoto, Caroline Vasconcelos, Daniel Magnobosco Marra, Jefferson Hall, Guillaume Tougas, and Isabelle Lefebvre. We also thank Mila for the compute resources.
Simone Aubé, Chloé Fiset,
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APPENDICES & SUPPLEMENTARY MATERIAL
A THE SELVABOX DATASET
A.1 ORTHOOMOSAICS.
The RGB orthomosaics were generated in Agisoft Metashape version 2.1. Images were acquired by flying at a constant elevation above the canopy. We kept a forward overlap of 80% and side overlap of > 70%. Images were acquired around mid-day to minimize shadows. Sky conditions ranged from full sun to overcast.
The main Metashape parameters used for all of our orthomosaic reconstructions were:
Alignment accuracy: High
Point cloud quality: High
Point cloud filtering: Disabled
Orthomosaic blending mode: Mosaic
Table 6: SELVABOX orthomosaics. We denote each type of DJI drone as 'm3e' for Mavic Enterprise, 'm3m' for Mavic 3 Multispectral, 'mavicpro' for Mavic Pro, 'mini2' for Mavic Mini 2.
Raster
Drone
County
Date
Sky conditions
GSD (cms/pp)
Forest type
#Hectares
#Amootations
Proposed splin(s)
dfQqudd
m3m
2026-01-30
primazy
d'lowwr
m3m
Brazil
2024-01-30
clear
primary
9.5
1716
test
dlrramooceww
m3m
Brazil
2024-01-30
cleur
1.5
primary
2.6
359
train
m3m
Amzil
2024-01-31
cleat
233
primary
66
163996
train
maviepro
2017-08-10
cloudy
primary
$119
2023-05-25
clear
primzry
1279
sunlower
mini?
Ecundor
2023-03-11
clouly
prinury
inundated
mile
Ecundor
2023-10-18
choudy
2.2
primary
68
9875
triin, valid, test
panzano
mile
Ecundor
2023-10-18
cloudy
19
primazy
41
4193
train
temafamme
mile
Ecuador
2023-10-18
clear
primazy
110
6479
soontheast
2023-12-07
plantations, sccondary
12030
valid,
soconhmorth
m3m
2023-12-07
cloud
plancations, sccondary
anforestnorthe?
m3m
Panama 2023-12-08
clear
secondary
20
5925
valid, cest
asforestooth22
m3m
Panama 2023-12-08
clear
1.5
secondary
28
10582
train
Table 7: SELVABox boxes details. Details of number of boxes for each raster, country and overall as well as their minimum, maximum and median box size expressed in meters.
Country
Location
Raster name
Boxes
size (m)
Max size (m)
Median box size (m)
202401 30 zf2quud m3m
1343
1.02
33.00
6.34
Brazil
ZF2
20240130. 101113302222aaa0 Jftramneettw.. mmm
359
0.90
26.94
5.12
20240131
//'campirana m3m
16399
0.93
36.72
6.01
20231018
inundared
9075
0.52
54.27
6.41
2023
pantano, m3e semafirme
4193
0.92
41 11.60
6.66
Ecuador
Agun Salud
201 20170810 transectatoni maviiepo
5119
0.83
47.97
5.80
20230525 201225___saa___e_ee_ _m3e
1279
1,16
41.28
8.45
12200911sanitwwerr
All rasters
27866
0.52
57.16
6.31
2023 1208 Jeforestnotthe2 mm3
5915
0.51
36.17
4.99
aasnortheust amsunclouds
Panama
Agun Salud
2023120 asnorthnarth, pmelonds, mšm
6020
0.50
29.28
4.63
2023 ssfooesssosshh2..oo
10582
0.83
38.92
4.83
rusters
AlI
All
All rasters
83137
0.50
$7.16
5.44
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A.2
SPATIALLY SEPARATED SPLITS.
zf2quad
zf2tower
22transectew
#2campirana
transectatoni
tbslake
sanitower
inundated
pantano
terrafirme
asnortheast amsunclouds
cettthhortt_pmccloue
asforestnorthe2
asforestsouth2
train
valid
test
Figure 5: Visualization of spatially separated splits. All 14 rasters of SELVABOX illustrated
with their corresponding train, valid and test AOI-based splits. Images are uniformly sized and not at scale. A few train AOIs (red) have holes exclude sparse annotations (see Section 3).
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A.3 ANNOTATION PROTOCOL
The annotations were created by five domain experts with exact same instructions, and all started with a demo and annotation practice beforehand. All annotations version 3.0 with AreGIS Online layers to track the online work of two annotators working on the same orthomosaic simultaneously. In large and dense areas, one or several annotators performed an additional pass over the orthomosaic to annotate potential missing trees.
made in AreGIS Pro
Once annotations were completed by one several annotators, one two domain experts performed quality control steps for all annotations of each orthomosaic by following precise guidelines:
1. Set up 60x60 m grid over the orthomosaic.
2. Proceed to the verification by systematically scanning each cell to avoid missing any areas.
3. Ensure that there are as many annotated trees as possible in each cell.
4. Also annotate dead or leafless trees.
5. Check that annotations already completed are correct, adjusting them if necessary.
All annotators and reviewers were provided with documentation covering difficult use cases reference when they were uncertain about the annotation procedure. For example, they were asked to discuss difficult cases with each other and reach consensus, particularly for ambiguous situations such as intertwined crowns, branches adjacent to large crowns that may correspond to separate understory trees, or vegetation that could be lianas rather than individual trees. Some variability in bounding box tightness (slightly more or less padding around crowns) is also expected. However, none of these concerns were flagged as significant during our systematic quality control.
As comparison, point out that annotations in OAM-TCD -Veitch-Michane et al., 2024) (NeurIPS 2024) were created by professional annotators who were not domain experts, and only a portion of these annotations reviewed by ecologists.
A.4 INCOMPLETE ANNOTATIONS.
Figure 6: Example of masked pixels in sparse annotations zones. Example on a 3555 x 3555 pixels training tile (160 160 meters) from the pantano raster. On the left the raw tile, showing holes (red polygons) in the train AOI geopackage where annotations (white boxes) are sparse. On the right the preprocessed tile, where pixels overlapping the AOI holes have been masked to remove sparse annotations. AOI holes were created mostly where visible trees were not annotated (see Section 3).
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B HYPERPARAMETERS AND AUGMENTATIONS
B.1 AUGMENTATIONS
For all experiments, we use the same set of basic augmentations:
Table 8: Settings of data augmentations used for all experiments. Augmentations were applied in the top to bottom order of the table. The Hue augmentation is applied to pixel values in the 0-255 range. The fallback value column describes the behavior of the preprocessing pipeline when an augmentation is not applied. Multi-dataset models use the multi-res. variants of crop and resize augmentations. The "spatial-extent" for our single-res. experiments on SELVABOX either 40 m or 80 (see Tab. 2 and 3). The crop augmentation for the multi-res. settings is expressed in pixels, where the value is randomly drawn between mmin and maa that will correspond to different spatial extents depending
the dataset (see Fig. 7 and Sec. F.I). The resize augmentation will either be applied with a fixed value y, expressed in pixel, for the single-res. applications on SELVA Box, or randomly drawn between Ymin and ymax for the multi-resolution and multi-dataset training approaches.
Augmentation
Probability
Augmentation Range
Fallback value
Flip Horizontal
0.5
Flip Vertical
0.5
Rotation
0.5
[-30%, +30%]
Brightness
0.5
[-20%, +20%]
Contrast
0.5
[-20%, +20%]
Saturation
0.5
[-20%, +20%]
Hue
0.3
[-10, +10]
Crop (single-res.)
0.5
spatial extent x [-10%, +10%]
spatial extent
Crop (multi-res.)
0.5
[Tminn max
max. image size
Resize (single-res))
1.0
Resize (multi-res.)
1.0
[ymin Fmnx
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B.2 TRAINING HYPERPARAMETERS
This section lists the hyperparameters found for each of our settings. We performed grid search (es 10 hyperparameter combinations) for every setting on four hyperparameters the learning-rate, its scheduler, the total number of epochs and the batch size. We left all other hyperparameters at their default values as specified in Detectron2 and Detrex configuration files. CosineLR refers to cosine learning-rate schedule without restart, We applied 5 000-step warmup at the start of cach training session. Training was performed on either 48 GB NVIDIA RTX 8000 or L40S GPUs, depending on compute-cluster availability. Most sessions used one or two GPUs; however, DINO + Swin L-384 with large input sizes, multi-resolution, or multi-dataset settings required four GPUs (one image per GPU per batch) due to their high memory footprint.
Table 9: Hyperparameters selected for the input size and GSD experimental analyses SELVABOX. Hyperparameters selected for each method and spatial extent in Tables 2 and 3. An initial search shown that, for each architecture and spatial extent, the optimal hyperparameters were nearly identical across GSDs; accordingly, we applied the same settings to all GSDs within each spatial extent.
Method
Extent (m)
Optimizer
LR
Scheduler
Max Epochs
Batch Size
Faster R-CNN (ResNet50)
SGD
10-3
Lo
500
8
DINO 4-scale (ResNet50)
40
Adam
10-4
CosineLR
4
DINO 5-scale (Swin L-384)
40 x 40
AdamW
5 x 10-3
CosineLR
500
8
Faster R-CNN (ResNet50)
SGD
5 10
CosineL
500
4
DINO 4-scale (ResNet50)
Adam
10
CosineLR
500
4
DINO 5-scale (Swin L-384)
80 x 80
AdamW
1 x 10-4
CosineLR
500
4
Table 10: Hyperparameters selected for the multi-resolution experimental analysis on SELV- A Box. These hyperparameters were optimal as being the same ones as used for DINO 5-scale (Swin L-384) at 80 x 80 m spatial extent. The associated models performance are in Figures 3, and Table 18.
Method
Train Crop Range (m)
Optimizer
Scheduler
Max Epochs
Size
DINO 5-scale (Swin L-384)
[36. 88)
AdamW
1x 10
Cosinel.R
500
DINO 5-scale (Swin L-384)
330,
Adam
1x 10-1 4
CusineLR
500
DINO 5-scale (Swin L-384)
[30,
Adam
10-4
CosineLR
500
Table 11: Hyperparameters selected for the OOD experimental analyses with multi-dataset trainings. For the MultiStepL.R scheduler, we reduced the learning rate by a factor of 10 at 80% and again at 90% of the total training epochs. The associated models performance are in Tables 4 and 5.
Method
Train Datasets
Optimizer
Scheduler
Max Epochs
Batch Size
DeepForest
N
N/A
N/A
N/A
N/A
N/A
Faster R-CNN (ResNet50)
N
SGD
5 10
CosineLR
500
DINO S-scale (Swin L-384)
N
AdamW
10
CosineL
80
DeepForest
S
SGD
5 10-3
CosineL
500
Faster R-CNN (ResNet50)
S
SGD
5 10-3
CosineLR
500
DINO S-scale (Swin L-384)
S
AdamW
1 x 10
CosineLR
500
4
DeepForest
N+Q+O
SGD
10-3
CosineL. R
200
Faster R-CNN (ResNet50)
N+Q+O
SGD
10
CosineLR
200
DINO S-scale (Swin L-384)
N+Q+O
AdamW
10
CosineI.R
80
DoepForest Faster R-CNN (ResNe150)
N+Q+O+S
SGD
5 x 10-3
CosineL
120
N+Q+O+S
SGD
5 x 10
CosineLR
120
DINO S-scale (Swin L-384)
N+Q+O+S
AdamW
1 1 10
MultiStepLR
80
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B.3 INFERENCE HYPERPARAMETERS
We detail the pseudocode for the 7F1 metric in Algorithm I (see Section 4). Setting Tiou = 0.75 corresponds to RF1 75. Before applying the NMS, discard predictions whose bounding box lies within a 5%dwide band along the tiles borders. We perform a grid search on the valid set over the non-mane nn--maximum suppression IoU threshold Tams and the minimum detection confidence score Smin+ each taking values in the discrete set {0.00, 0.05, 0.10, 1.00]. We multiprocess the grid search on 12 CPU cores to speed up the process. After finding the optimal Trme and Smin on the best model seed, we apply it on the test set to all model seeds to compute the final RF1 score with standard deviation.
Algorithm Per-dataset evaluation with weighted RFI
Require: Dataset D rasters, detector M, Trms, 8mins Tiom
1: R +-
>. list of per-raster Fl scores > list of per-raster truth counts
2: W +-0 0
3: for each raster r D do
4:
Pe@
> accumulate tile preds load geo-truth
5:
G [-oadGrrumdTruth((
6:
for each tile do
7:
p --Mppeddcc(()) P+ PUp
8:
9:
end for
10:
Poont + {p E P: p.score 2 sminl P' +- NonMaxSuppression Poonf Tmms)
12:
(Ip, Sp, fn) <- GreedyMatch G, Tiou)
precision ttpp(tp fP)
14:
recall <- (tp
15:
fl +-2 2 precision recall
precislon+recal
G
truth count
17:
R + RUfl
18:
WUn
19:
end for
20:
+ Enew n
RF1 1
ΣR R
R₂ W,
W
22:
store weighted- RFI
Algorithm 2 Greedy matching for RF1
1: procedure GREEDY G. Tiou)
2: sort P' by descending score
3: mark all g E G unmatched
4: tp <- 0, Sp < 0
5: for each prediction p P' do
6:
g" + arg max uomatched=ttme loU(p.g)
7:
if LoU 2 Tiou then
8:
tp t tp 1
9:
mark g" as matched
10:
else
11:
+ ++++++
12:
end if
13:
end for
14: fn + [g E G gummatcheed true]
15:
return (tp, fp, fn)
16:
end procedure
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Table 12: Optimal inference hyperparameters for the input size and GSD experimental analysis at 40 x 40 meters on BEX.ABOX. Both optimal NMS and score thresholds are selected by maximizing the RFI 75 metric described in Algorithm 1. The associated models performance are in Table 2.
Method
GSD
size
NMS IoU (Tams)
Score thr. (5min)
400
0.50
0.85
666
0.60
0.70
888
0.50
0.80
Faster RCNN
666
0.55
0.90
ResNet50)
888
0.70
0.90
4.5
888
0.65
0.85
10
400
0.70
0.45
10
666
0.50
0.35
10
888
0.75
0.35
DINO 4-scale
666
0.65
0.45
ResNe150
888
0.35
0.35
888
0.65
0.40
10
400
0.75
0.35
10
666
0.80
0.45
10
888
0.35
0.35
DINO S-scale
666
0,55
0.35
Swin L-384
888
0.45
0.40
4.5
888
0.50
0.35
Table 13: Optimal inference hyperparameters for the input size and GSD experimental analysis at x0x80 meters on SELVABOX. Both optimal NMS and score thresholds are selected by maximizing the 75 metric on the validation set SELVA Box as described in Algorithm The associated models performance are in Table 3.
Method
size
NMS IoU (Fams)
Score (8min)
10
800
0.70
0.75
10
1333
0.40
0.70
10
1777
0.35
0.60
Faster RCNN
1333
0.40
0.70
ResNe150
1777
0.45
0.75
4.5
1777
0.25
0.35
10
800
0.35
0.45
10
1333
0.75
0.45
10
1777
0.70
0.40
DINO 4-scale
1333
0.35
0.40
ResNet50
1777
0.75
0.35
4.5
1777
0.40
0.35
800
0.75
0.35
1333
0.80
0.40
1777
0.70
0.35
DINO 5-scale
1333
0.75
0.45
Swin L-384
1777
0.65
0.35
4.5
1777
0.75
0.45
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Table 14: Optimal inference hyperparameters for the multi-resolution experimental analysis on SELVABOX. Both optimal NMS and score thresholds selected by maximizing the RF1 75 metric on the validation set of SELVABOX as described Algorithm 1. in Figures 3, 8 and Table 18.
The associated models performance
Method
Train Crop Range (m)
Test GSD (cm)
NMS loU (Fams)
Score thr. (fmin)
10
0.70
0.45
DINO Swin L-384
[36, 88]
4.5
0.70
0.45
DINO 5-seale Swin L-384
30, 100]
0.70
0.40
4.5
0.60
0.40
10
0.70
0.40
DINO 5-scule Swin L-384
30, 120]
0.50
0.35
4.5
0.40
Table 15: Optimal inference hyperparameters for the experimental analyses with multi-dataset trainings. Both optimal NMS and score thresholds selected by maximizing the RFI metric on the validation sets of both SELVABOX and Detectree2 as described in Algorithm 1. The associated models performance are Tables and 5.
Method
Train dataset(s)
NMS IoU (Tams)
Score thr, (Sasiu)
Detectree2-resize
D
0.30
0.25
Detectree2-flexi
Deurban
0.20
DeepForest
0.05
R-CNN-ReaNee50
N
0.10
0.50
DINO-Swin-L
N
0.80
0.55
DeepPorest
S
0.30
0.40
E.
R-CNNN-R
S
0.20
0.45
DINO-Swin-L
0.40
DeepForest
N+Q+O
0.70
0.40
R-CNN-ResNet50
N+Q+0
0.50
0.45
DINO-Swin-L
N+Q+O
0.70
0.40
DeepPorest
N+Q+O+S
0.30
0.40
F. DINO-Swin-1
N+Q+0+S
0.20
0.50
N+Q+O+S
0.70
0.50
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Published as a conference paper at ICLR 2026
C BENCHMARKING RESOLUTIONS AND IMAGE SIZES
Table 16: Model, resolution and spatial extent selection on SELVABOX at 40 x 40 m. Comparison of performances the proposed test set of SELVA Box with variable tile spatial extent. Tile size and ground spatial distance (GSD) are in cm. We highlight results per method and backhone as the first, the second and the third best scores. We also bold and underline the best and second best scores overall. Note that mAP50. mAP50:955 mA and mA cannot be compared between 40 x 40 m and 80 x 80 m inputs as images do not match, but we can use RF175 to compare final gost-aggregation results at the raster-level.
Method
GSD
1. size
mAPs)
mAPs0-85
mARso
mAR50:95
RF
10
400
54.92 (#0.08)
26.90 ct0 13)
74.48 (=0.42)
40.87
(#0.35)
35.78 (+0.44)
10
666
57.03 (20000)
28.40 (z0
76.53
42.79
(20.19)
37.75 (d0-50)
Faster RCNN
10
888
56,42 (1:0.00)
28.51
(10. 20)
76.21 (20.14)
43.36 (10.19)
37.46 (10.991)
ResNet50
666
$7.13
(1:0.7))
29.31
(1.0.5))
76.25
(=0.00)
43.59
(1.0.20)
39.97 (1.0.33)
888
57.27
(#0.54)
29.40 (4:0.
77.26
(=0.77)
44.18
(#00.4)
38.92 (4:0.51)
888
58.33
(40.21)
30.25 (#0. 24)
78.41
(=0.15)
45.18 (#00300)
39.97 (40.67)
10
400
56.98 (4:0.25)
30,63 (4:0. 24)
76.92 (:7774)
48.06
(::0333)
41.14 (4:0.80)
10
666
57.62 (#0064)
31.76 (#:0.80)
78.56 (=0.16)
$0.40 (#0.55)
41.57 (#:1.4)
DINO 4-scale
10
888
58.
(c:0664)
32.19 (t00.
78.55 (=0.34)
50.68 (#00.19) 19)
42.47 (+00997)
ResNet50
666
58.71
(#:0.34)
33.46 (tb0. 22)
78.95 (=0.20)
$1.80 (+:0.3))
44.55 (80.11)
888
58.78 (200.50)
33.54 cio 40)
79. 16 0.tb
52.12 (2:0. 18)
43.34 (20079))
888
60.11 (100.30)
(110. 13)
(.0.15)
52.53 (1.00.0 40)
(1.0.83)
10
400
60.44 (20.39)
33.84 <::0. 20)
99.84 (=0
52.02 (2:0225)
45.37 (b0:22)
666
61.26 (1:0.30)
34. 64.64 (1:00 25)
80.77 (2))77
52.91 (100330)
46.39 (110.52)
10
888
61.06 (1:0.53)
34.92 (1.0.3))
80.70 (20.13)
$3.23 (100.4)
45.22 (10.70)
DINO 5-scale
Swin L-384
666
62.91 (#:0.4))
37.07 (4:0.16)
81.58
(=0.12)
55.18
(#0.22)
48.50 (4:0.60)
888
62.45 (+0017)
36.22 (#00.38)
81.47 (=0.18)
54.55 (#0.44)
48.13 (#0.60)
4.5
888
63.41 (#0.200
37.78 (tb 15)
82.33 (=0.55)
56.30 (20.23)
49.76 (b0.45)
Table 17: Model, resolution and spatial extent selection on SELVABOX at 80 x 80 m. Comparison of performances on the proposed test set of SELVABOX with variable tile spatial extent. Tile size and ground spatial distance (GSD) are in cm. We highlight results per method and backbone as the first, the second and best scores overall. Note that mAP30. mAP30:95, mAR and mAR50:95 cannot be compared between 40 x 40 m and 80 x 80 m inputs as images do not match, but we can use RF175 to compare final post-aggregation results at the raster-level.
the third best scores. We also bold and underline the best and second
Method
GSD
1 size
mAP
mAP30.5
mAR5o
mAR
RF1
10
800
50,50 (10044)
24.94
(10.39)
64.72
(=1.25)
35.93
(10.30)
34.66 (10:57)
10
1333
51.37 (10.11)
26.25 (10014)
67.57
(=0.53)
38.59 (10.43)
36.09 (1.0.51)
Faster RCNN
10
1777
54,20 (#0.55)
27.58
(4:0.4))
70.65
(=1.84)
40.21 (#0.35)
35.74 (4:1.26)
ResNe150
1333
51.96
(#0.64)
26.52 (#0.80)
69.77
(=1.88)
39.55 (#0.75)
36.22 (+:145)
1777
54.68 (#0.36)
27.89
(t0
72.32
(=1.85)
41.02
(20060)
35.94 (+0.84)
4.5
1777
56.21
(2:0. 761
28. 78.74 (20
72.12
(=0.70) 70)
41.27
(2:0.59)
37.52 (30055)
10
800
58.32 (1.0.44)
30.90 (110.51)
76.33
(20.25)
47.29
(110.33)
41.20 (.10.39)
10
1333
59.65 (::0.20)
32.39 (#0.02)
77.61
(=0.07)
49.22
(2:0 10)
43.08 (t0.220)
DINO 4-scale
10
1777
59.31
(::1 1.29
32.51 cbo s00
77.23
(20.30)
49.35
(20047)
42.39 (K1125)
ResNet50
1333
59.8
(110. 422
33.06 (1.0. 29)
77.91
(20.17)
49.93
(1.0339)
42.92 (1.0.51)
1777
60.48
201
62 (1:0. 10)
78.32
(=00211
$0.85 (10..77)
44.18 (10.115)
4.5
1777
61.09 (500.45
33.81 $1 (#00.
78.93
(=0.02)
$1.00
(20.77)
43.26 (4:0.45)
10
800
62.02 (#0.08)
33.90
(t0.000 09)
78.89
(=0.22)
50.29
(t0.38)
44.64 (t00220)
10
1333
61.73 (2:0.2 72)
34,22 (20035))
79.03
(20.87)
50.76
(2:0.57)
45.66 (t:1-9))
DINO 5-scale
10
1777
62.86
(1.0.75)
35.30 ciro 26)
79.9 94
(200.88) (88)
$2.12
(100 o12)
45.37 (10000)
Swin L-384
1333
64.91
(1.0.30)
37.12 (1.0 38)
81.01
t (0))
53.56 (L.0.45)
47.81 (10.40)
1777
63.34 (40.58)
35.77 (#40.4)
80.59
(=0.100)
52.91 (#:0.56)
45.88 (4:1.97)
4.5
1777
64.59 (111000)
37.79
81.35
(20.71)
$4,66
49,38 (10.76)
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D MULTI-RESOLUTION APPROACH
D.1 MULTI-RESOLLTIIO EXAMPLE
666x666 pixels crop, upsampled to 1024x100 1224 30x30 meters
2.9 cm/pixel (bilinear interp.)
666x666 pixels crop
30x30 meters
4.5 cm/pixel
666x666 pixels crop, upsampled to 1777x1777 +0xx30 meters
1.7 cm/pixel (bilinear interp.)
2666x2666 pixels crop, downsampled 1024x1024 + 120x120 meters 11.7 cm/pixel (bilinear interp.)
3555x3555 pixels tile
+160x160 16 meters 4.55 cm/pixel
2666x2666 pixels crop
+ 1200x110 meters
+ cm/pixel
2666x2666 pixels crop, downsampled to 1777x1777 + 120x120 meters
+ 6.8 cm/pixel (bilinear interp.)
Figure 7: Example of cropping and resizing augmentations for the multi-resolution approach. We showcase the [30, 120) m configuration used in our benchmark: 3555 x 3555 tile at 4.5cm 0.045 m GSD, equivalent to 160 160 m spatial extent, will be cropped with random crop size value in [666, 2666) pixels, and then resized random value in [1024, 1777] pixels. This process has two effects: cropping performs augmentation for spatial extent in our example, the original input has the potential to be cropped in ground extent range of [30, 120) m; 2 resizing performs the GSD augmentation in our example, the largest possible crop (in blue) of 2666 pixels (or 120 m) be downsampled 1024 x 1024, which yields maximum effective GSD of 0.045 m x 2666 1u24 0.117 m 11.7 em per pixel, far from the original 4.5 em per pixel. Similarly, the smallest possible crop (in orange) of 666 pixels (or 30 m) can be upsampled
1777 x 1777 pixels, yielding a minimum effective GSD of 0.045 m X 1777 = 0.017 m = 1.7 em per pixel. Note
666
that for small crops, the effective GSD after upsampling (via bilinear interpolation) can fall below the original 4.5 cm/pixel, even though no new image detail is added.
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D.2 MULTI-RESOLUTION ADDITIONAL RESULTS
55
mAPs0:0s Score
Scoor
50
45
mARg0
25
singleres. 4, 5.5cm
single reseeres..e 5.5mm
single res. 6cm
40
single res. 5cm
10cm
[36. 88]m
multires, 883m
multi res. [30. 100
35
multi-rss [30, 1003m
multi-nes. [30, 120
multi-es. [30, 120
10
Ground Sampling Distance (cm/px)
Ground Sampling Distance (cm/px)
Figure 8: Multi-resolution vs. single-resolution on SELVA Box. Comparison of mAP50:90 and mARs0:95 between best performing single-resolution methods from Table 3 trained with fixed spatial extent of 80 x 80 m, against multi resoutinso approaches with increasingly large crop augmentation ranges (136, 88], [30, 100) and [30, 120)). All methods are 'DINO 5-scale Swin L-384'. It supports results illustrated Figure 3.
Table 18: Multi-resolution vs. single-resolution SELVA Box. Comparison of best performing methods from Table trained with a fixed spatial extent against multi-resolution approaches. All methods are 'DINO 5-scale Swin L-384', have been trained at 4.5cm. We mark the best and second- best scores bold and underline, respectively. These results are also illustrated in Figures and
Train extent (m)
Test extent (m)
Test (cm/px)
mAP50
mAP
mAR30
mAR50:95
RFI 75
80
80
10
62,02 (1.008))
33,90
78.89 (1.0.22)
50.29 (10.38)
44,
80
80
64.91 (2:030)
37.12
81.01 (20.00)
53.56 (20.48)
47.81 (20.40)
80
80
4.5
64.59 (#:1.08)
37.79
(40.50)
81.35 (10.71)
54.66 (#0.47)
49.38 (50.76)
80
10
63.33 (#0.48)
34.19 (#0.44)
79.98
(=:0211)
50.99 (=0.41)
45.03 (#0.53)
[36, 88] J [160)
80
65.38 (1.0.11)
36.60 60. (:1.38)
81.29
52.95 (d:1.47)
47.87 (10.92)
80
4.5
65.68 (2:0.00)
38.19 (20.54)
81.85
54.90 (10..59
49. (20006)
80
10
62.52 (#1330)
33.82 (#0.74)
79.42 (#:0. 35)
50.52 (+0.35)
44.13 (#0-79)
330, 100 (160)
80
64.70 (1.0.8))
36.46
(40..99
80.99
52,99
47. 96
80
4.5
61. (2:0.88)
37.77 (20.96)
81.47
54.68 (20.47)
48.79 (20051)
62.76 (10.49)
33.99 (10.35)
79.51
(1.00 08)
50.66 (L0.00)) 08)
(110655)
[30, 120] (160)
80
64.44 (20.26)
36.08
55)
80.68 (::0 (20.42)
52.64 (22.00)
46.65 (#1.67) (67)
80
4.5
64.92 (50.63)
37.77
81.19
08)
54.69 (e0.07)
48
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Published as a conference paper at ICLR 2026
E ABLATION STUDY ON RF1 IOU THRESHOLD
RF1yS
DIN Swin (MOOS)
Octectrec2 restee
Detectrez2
50
55
60
03
70
75
80
85
90
95
threshold
Figure 9: RF1 vs IoU threshold on BCI50ha. Comparison of of our DINO-Swin-L variants and competing methods at different IoU thresholds. In this work we focus on RF1 (loU=0.75). For each loU threshold, NMS hyperparameters are re-optimized on the validation set.
RF1ys
LPDD-SwF-4 (M025)
50
Derectrew2 ees
Deractrree
40
10
0-
50
55
80
05
RF1 IoU threshold
Figure 10: RF1 vs IoU threshold on Detectree2. Comparison of two of our DINO-Swin-L variants and competing methods at different IoU thresholds. In this work we focus on RF1 (loU=0.75). For each IoU threshold, NMS hyperparameters are re-omiimieed on the validation set.
70
RF 8F1
DINO Swir (M225)
Detectroo resios
Dettectrie
60
30
20
50
55
60
70
80
85
RF1 loU threshold
Figure 11: RF1 vs IoU threshold on QuebeeTrees. Comparison of two our DINO-Swin-L variants and competing methods at different IoU thresholds. In this work focus RF (1oU=0.75). For each IoU threshold, NMS hyperparameters are re-optimized on the validation set.
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F OUT-OF-DISTRIBUTION ANALYSIS
F.1 EXTERNAL DATASETS PREPROCESSING
For NeonTree TreeFvaluation, we keep the proposed 400 x 400 pixels test inputs at c0 cm GSD and define train and validation AOIs on their rasters. Similarly, for QuebecTrees, AOI while defining our own train and validation AOIs. As Detectree2's s train, validation, and test splits are not shared publicly, we defined our own validation and test AOIs, while keeping the input size as 1000 x 1000 to follow their guidelines. BCI50ha is only used for OOD evaluation (see OOD experiments in Sections 4 and 5), we define test AOIs spanning both rasters.
keep the proposed test split
OAM-TCD contains two types of annotations: individual trees and tree groups. Unfortunately, tree groups would introduce noise during the training process as all other datasets focus on individual tree detection. Therefore, we only consider individual trees annotations and we mask the pixels associated to tree groups from the training data to ensure consistency. This process is similar to how we mask specific low quality pixels and sparse annotations in SELVABOG BOX as detailed in Section 3. OAM-TTC provides five predefined cross-vas valiiaaiinn folds; we train on folds 0-3 and use fold exclusively for validation. We further divide the 2048 x 2048 validation and test tiles of OAM-TCD into 1024 x 1024 tiles with 50% overlap, as 204.8 x 204.8 m GSD would be significamtly larger than other datasets. We refer Table 19 for more details final preprocessed datasets statisties and information.
For each dataset divided into tiles, we apply the same AOI-based pixel masking, black/whiiettranspareett pixel cover threshold, and O-annotation tile removal, as described in Section 3. We use 50% overlap between tiles for all datasets for which we divided rasters into tiles, except BCI50ha where we use 75% to maximize cover for 50+ meters tree crowns (same as SELVABox test split). We also release these preprocessed external datasets on HuggingFace, including the proposed AOIS and raster-level annotation geopackages for all datasets, in standardized MI.-ready format and with their original CC-BY 4.0 license to ensure reproducibility of our benchmark and facilitate experiments of researchers and practitioners for tree-crown detection. We used version 1.0.0 of OAM- TCD, 4 version vI of QuebecTrees, version v2 Detectree2, version 0.2.2 of Treeavauvalnnnnnnnnn and version 2 of BCI50ha."
Table 19: Preprocessing and training parameters for all datasets used. The SELVABOX parameters correspond to the [30, 120] m multi-resolution setting. Although test tiles outnumber training tiles numerically, training tiles are deliberately larger in spatial extent to facilitate augmentation strategies, resulting in greater total geographic coverage within the train split. The minimum effective train resolution range is reached by using bilinear interpolation from the smallest possible crop size to the largest possible input resize value. *At training time, we resize Neon Evaluuautiio training tiles to 2000 pixels before cropping to ensure that the effective train extent range reaches the 40 m used in the test split.
GSD (omslpx)
T'aiin Images
Augm, Crop
Augm.
train res. range (cmilpx)
Test (px)
(px)
range (px)
range (px)
extent range (m)
Images
(01)
Noww Evalltttto
912
[666, 3666]
[1024, 1777]
120]"
11.7)
OAM-TCD
10
3004
2048
[666, 2666]
[1024, 1777]
[96.6, 204.8)
[388,
1024
102
QueeerTrees
148
[666, 3666]
[1034, 1777]
[20, 50 1000
[.11998)
1666
50
SELVABox
4.5
535
3555
[666. [666]
[1024, 17771
[10, 120 (100)
[1.7. 15.6)
1472
1777
80
Dateenec2
10
NIA
N/A
NIA
N/A
N/A
N/A
311
1000
100
BC1500
4.5
NIA
N/
NIA
N/A
N/A
NAA
2706
1777
801
"Detectree2; http::////eeoobboggg/eeooooooooo
NNoon TreeFualuttion:: https:///anooboorg//eeee.
BCT50haa Smithsonian Barro Colorado Island 50-ha plot crown maps
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100000
Datasets
QAM-TCD
SelvaBox
QuebecTrees
NeenTreeEvaluat
RC150ha
Detectree2
1000
Number objects
100
6-8
8-10
10:12
12.14
14-10
16-19
18-20
20.22
22.24
24:30
20
-0
32:34
38:30
36 3-33
38 40
42
44-46
46-40
46-50
50
42
Box side length (m)
Figure 12: Distribution of box annotations size across datasets.
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F.2 STATISTICAL COMPARISON BETWEEN DATASETS
To rigorously compare SelvaBox with existing datasets, we quantified crown-size distribution differ- ences using three complementary statistical approaches:
Methodology We computed the -hasen-Shannon (JS) distance and Kullback-Leible (KL) diver- gence across datasets. Since KL. divergence is asymmetric and unbounded, making interpretation difficult, we prioritize JS distance, which is symmetric, bounded between 0 and 1, and well-suited for discrete distributions. We also performed two-sample Kolmogorov-Smiman (KS) tests comparing SelvaBox against existing datasets. The KS test evaluates the maximum vertical distance between empirical cumulative distribution functions (ECDFs), providing non non-parametric distribution
measure robust to differences in dataset size. Given that KS test p-values follow R 2e -2n-D" Marsaglia al. (2003), where is dataset size and D the KS statistic, and our dataset sizes range from n 3. 947 (Detectree2) to 266, 663 (OAM-TCD), we expect p-values approaching 1.04 104000 10-35 when 2 0.1 and 3, 947.
Results SelvaBox exhibits substantially different crown-size distributions from tropical datasets (IS Distance: BCI50ha = 0.6248, Detectree2 = 0.6476) and moderately different distributions from temperate datasets Treevvautvaittio 0.2789, QuebecTrees 0.2622). Pairwise KS tests reveal highly significant differences across all comparisons (p < 0.001, KS statistics ranging from 0.2117 0.6338), confirming that SelvaBox's crown-size distribution is statistically distinct from all existing datasets. The large annotation counts (3,947 to 266,663 samples) ensure these distributional differences are meaningful and robust to dataset scale variations.
Interpretation OAM-TCD shows the greatest similarity to SelvaBox (JS Distance 0.2152), likely because its large geographic scale and multi-biome coverage encompass diverse crown morpholo- gies, unlike datasets restricted to single regions biomes. The asymmetric KI. divergence values further support these conclusions, demonstrating how SclvaBox uniquely captures the crown-size distributions and structural diversity of tropical forests.
Table 20: Statistical comparison of crown-size distributions between SELVABOX and existing datasets. Higher IS distance, KL divergence, and KS statistics indicate greater distributional differences, while very small KS p-values indicate that the null hypothesis of identical distributions can be rejected.
BC150ha
Detectree2
NeonTreeEval. QuebeeTrress
OAM-TCD
Distance
0.6248
0.6476
0.2789
0.2622
0,2152
KL Divergence
2.4293
2.1779
0.3826
0.7028
0.1687
SelvaBox
KS Test
0.6231
0.6338
0.3257
0.2270
0.2117
KS Test p-vuuue
< 10-30
< 1- 10-35
< 1 10-35
< 1 10-30
< 1- 10-0030
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F.3 EXTERNAL METHODS EVALUATION
We keep the default Detectree2 inference parameters provided in their python library. For DeepForest, their python library directly benchmark their method but limit input size 1000 x 1000 pixels maximum following their documentation guidelines and examples.
F.4 CEFFRESSRRE DATASET QUALITATIVE RESULTS.
Fine-tuned DeepForest Annotations
Multi-resolltion [30, 120)
Multi-cataset (N+Q+0+5)
Figure 13: Qualitative results on ReforesTree. In white the ReforesTree annotations generated from an in-distribution and fine-tuned DeepForest model, in blue our best multi-resolution [30, 120] model and in red our best model trained on multi-dataset + SELVABOX (both our methods are OOD). Results are shown post-NMS, using the optimal NMS IoU (Tums) and score (smin) thresholds for RF175 from Algorithm 1 (see Section B.3 for exact values). These examples illustrate the superior detection performance of our DINO-Swin models compared Refores annotations, especially for larger trees.
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F.5
TROPICAL DATASETS QUALITATIVE RESULTS.
Annotations
Detectree2-resize
Multi-resolution [30, 120]
Multi-dataset (N+Q+0+5)
Figure 14: Qualitative results on SELVABOX (Brazil). We compare the annotations in white, the best competing method Detectree2-resize (OOD) in yellow, our best multi-resolltion [30, 120] model (ID) in blue and our best model trained on multi-dataset + SELVABOX (ID) in red. Results are shown Post-NMS, using the optimal NMS loU (Toms) and score (smin) thresholds for RF1 from Algorithm (see Section B.3 for exact values).
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Annotations
Detectree2-resize
Multi-resolution [30, 120]
Multi-dataset (N+Q+O+S)
Figure 15; Qualitative results SELVABOX (Ecuador). We compare the annotations in white, the best competing method Detectree2-resize (OOD) in yellow, our best multi-resolution [30, 120] model (ID) in blue and our best model trained on multi-dataset SELVABOX (ID) in red. Results are shown post-NMS, using the optimal NMS loU (Toms) and score (8min) thresholds for RF1 from Algorithm (see Section B.3 for exact values).
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Annotations
Detectree2-resize
Multi-resolution [30, 120]
Multi-dataset (N+Q+0+S)
Figure 16: Qualitative results SELVABOX (Panama). We compare the annotations in white, the best competing method Detectree2-resize (OOD) in yellow, our best multi-resolution [30, 120] model (ID) in blue and our best model trained on multi-dataset SELVABOX (ID) red. Results are shown Post-NMS, using the optimal NMS loU (Fuma) and score (smin) thresholds for RF1 from Algorithm (see Section B.3 for exact values).
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Annotations
Detectree2-resize
Multi-resolution [30, 120]
Multi-dataset (N+Q+O+S)
Figure 17: Qualitative results on BC150ha. We compare the annotations in white, the best competing method Detectree2-resize (OOD) in yellow, our best multi-resolution [30, 120] model (OOD) in blue and our best model trained on multi-dataset + SELVABOX (OOD) in red. Results are shown NoSt-NMS, using the optimal NMS loU (Tnms) and score (8min) thresholds for RF1 from Algorithm (see Section B.3 for exact values).
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Annotations
Detectree2-resize
Multi-resolution [30, 120]
Multi-dataset (N+Q+O+S)
Figure 18: Qualitative results Detectree2 dataset. We compare the annotations in white, the best competing method Detectree2-resizz (ID; possibly affected by train-test leakage, since we couldn't 't recover their data splits) in yellow, our best multi-resolution [30, 120] model (OOD) in blue and our best model trained on multi-dataset + SELVABoX (OOD) in red. Results are shown Nost-NMS. using the optimal NMS loU (Tams) and score (8min) thresholds for RF1 from Algorithm (see Section B.3 for exact values).
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F.6 NON-TROPICAL DATASETS QUALITATIVE RESULTS.
Annotations
Detectree2-flexi
Multi-resolution [30, 120]
Multi-dataset (N+Q+O+S)
Figure 19: Qualitative results on QuebecTrees. We compare the annotations in white, the best competing method Detectree2-flex (OOD) in yellow, our best multi-resolution [30, 120] model (OOD) in blue and our best model trained on multi-dataset SELVABOX (ID) in red. Results shown Post-NMS, using the optimal NMS loU (Fums) and score (smin) thresholds for RF175 from Algorithm (see Section B.3 for exact values).
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Published as a conference paper at ICLR 2026
G PYTHON LIBRARIES
G.1 GEODATASET
We've released our pip-installable Python library geodataset on GitHub under the permissive Apache 2.0 license. The library serves four main purposes: 1 Tilerizers for cutting rasters into wiish resampling, AOI, and pixel-masking support- for training/evaluation (as COCO-style JSON) inference: 2 an Aggregator tool that converts predicted object coordinates back into the original CRS and efficiently performs NMS on large sets detections (at the raster-level;; 3 base dataset classes for training and inference that integrate easily with PyTorch's DataLoader; and 4 standardized conventions for naming tiles and COCO JSON files. See the repository documentation for more details.
G.2 CANOPYRS
We' ve released a Python GitHub repository called CanopyRS to replicate our results, benchmark models, and infer on new forest imagery. It's distributed under the permissive Apache 2.0 license and leverages geodataset for pre- and post-processing, with Detectron2 and Detrex handling model training. Its modular design makes it easy to extend in future work- example, supporting instance segmentation, clustering, or classification of individual trees. See the repository documentation for more details.
H USE OF LARGE LANGUAGE MODELS (LLMS)
used LLMs as general-purpon assistive tools to improve clarity and conciseness of text, as well as for occasional coding assistance. These tools were not used for research ideation to generate novel scientific content, All conceptual and experimental contributions were made by the authors.
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Reviews:
[{"review_id": "UaBolxyp0u", "reviewer": "Reviewer_6MAy", "summary": "This paper introduces SELVABOX, a large-scale dataset for individual tree crown detection in tropical forests, comprising 83,137 manually annotated bounding boxes from UAV imagery across Brazil, Ecuador, and Panama. The authors conduct extensive benchmarks comparing CNN-based (Faster R-CNN) and transformer-based (DINO) detection methods across multiple resolutions and spatial extents. Key contributions include: (1) the SELVABOX dataset with expert annotations, (2) a comprehensive benchmark including a new raster-level evaluation metric (RF175), (3) demonstration of multi-resolution training for cross-dataset generalization, and (4) open-source tools for preprocessing and inference. The work shows that DINO with Swin-L backbone achieves state-of-the-art performance on both tropical and temperate forest datasets.", "questions": "See above", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. High-quality dataset: Expert annotation by trained biologists (1,284 person-hours) across diverse tropical forest types ensures dataset quality and ecological validity\n2. Rigorous experimental methodology: Spatially separated splits, multiple resolutions tested, standard deviations reported, and extensive ablation studies demonstrate scientific rigor\n3. Reproducibility: Excellent documentation with code, data, and models publicly released; comprehensive appendices detail preprocessing steps for all datasets\n4. Raster-level evaluation: The RF175 metric addresses a real limitation of tile-level metrics for operational forest monitoring applications", "weaknesses": "1. Limited technical novelty: The paper is primarily an application/benchmark paper. While valuable, it doesn't introduce novel architectures or training techniques beyond straightforward multi-resolution augmentation\n2. Incomplete analysis of RF175 metric. No ablation on the 75% IoU threshold choice\n3. Experimental limitations: Missing temporal validation (same site, different time periods)\n4. Statistical rigor: Limited discussion of when differences are meaningful", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zIbJg6T1Jc", "reviewer": "Reviewer_QBSE", "summary": "This paper introduces a dataset, SelvaBox, containing UAV orthoimagery over several neotropical forest locations, covering 500 ha and containing 83k manually annotated tree crowns in the form of bounding boxes. Although larger datasets exist for temperate zones, this dataset aims at providing a large, diverse and high-quality crown detection benchmark for the tropics, with a focus on natural forests. The results show that using SelvaBox along with a transformer-based object detection model leads to state-of-the-art results on several other benchmarks. The paper also proposes an evaluation protocol, includinf the RF1_75 metric computer at the whole raster level as a better way to assess model performance.", "questions": "Would it still be feasible to look at inter-annotator agreement? Are some tiles maybe already annotated by two or more experts? Could experts be contacted to do this? I understand that this could be tricky to pull off in the latter case.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is clearly written and well structured.\n- The motivation is clear and well formulated.\n- The experiments are comprehensive and highlight the usefulness of the new dataset.\n- The choices in terms of dataset creation and models are well justified.", "weaknesses": "1. This work is quite complete. However, it all relies on the quality of the manual annotations. Although the good cross-dataset performance already indicates that the annotation quality is appropriate (and thus the within-SelvaBox performance does not rely on learning dataset-specific annotation biases), it would still be very helpful to see a quality assessment in the form of inter-annotator agreement analysis.\n2. The proposals of new evaluation metric/setup, which is one of the novelty claims in the paper, would be much better supported using such an inter-annotator evaluation, since they would act as an oracle predictor on which the new metrics could be compared to previous ones. Otherwise, there is little evidence that supports the claims about the proposed evaluation protocol.\n3. In a way, this paper presents a dataset where the main edge is that it is larger scale than others in the same (tropical forest) setting. As such, there is little novelty to speak of.  Novelty is typically a requirement according to the ICLR reviewer guidelines. I’m not 100% sure what this entails when it comes to datasets, but I would imagine enabling the benchmarking of so far un-benchmarkable tasks. SelvaScope does not allow to evaluate methods on anything that is fundamentally different, although its larger scale and diversity will likely be helpful to train models that will be useful to practitioners. As such, it maybe worth questioning the adequacy of ICLR as a venue to publish this paper, although I do commend the authors for the quality of their work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ptykREjMAR", "reviewer": "Reviewer_qCUJ", "summary": "This paper introduces **SELVABOX**, a large-scale UAV RGB dataset for detecting individual tree crowns in tropical forests across Brazil, Ecuador, and Panama. The authors also propose a practical evaluation metric, **RF175**, which measures raster-level F1 at an IoU threshold of 0.75. The dataset contains more than 83k high-quality crown annotations and comes with a complete processing pipeline, open-source code, and pretrained models.  \nExtensive experiments are conducted with both CNN and Transformer detectors. The results show that higher image resolution and multi-resolution training can significantly improve generalization, even across different domains. Overall, the dataset and benchmark make a timely and meaningful contribution to ecological AI and forest monitoring.", "questions": "1. Could you share some statistics on **annotation consistency**? Even small samples of double-annotated areas or agreement rates would help quantify data reliability.\n2. For the **test set completeness**, did you perform any internal audit or random visual checks? If so, please describe how it was done.\n3. Have you tried testing **RF175 under different IoU thresholds** (e.g., 0.6, 0.7, 0.8)? A sensitivity curve would be nice to include, even in the appendix.\n4. Regarding the **Detectree2 benchmark**, is there a chance of overlapping geographic regions? It might help to clarify this in the final version and mention it clearly as a limitation.\n5. Could you add a small **quantitative comparison with other datasets**, perhaps comparing crown-size or bounding-box distributions numerically instead of only visually?\n6. The paper would benefit from a short **Responsible Use or Ethics** paragraph in the main text. You could briefly outline intended use, potential misuse, and the licensing policy.\n7. It might also help to add **one or two failure case visualizations**, showing where the models still struggle (e.g., dense canopy overlap or mixed-species confusion). This would make the benchmark even more insightful.\n8. Have you considered adding **statistical confidence intervals** (like standard deviations over random seeds) for RF175 in the main tables? It would make the reported numbers more interpretable.\n9. Some references could be considered in Introduction and Related works: such as 10.1109/MGRS.2024.3479871, https://doi.org/10.1016/j.rse.2023.113485 and https://doi.org/10.1016/j.isprsjprs.2020.07.002", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **High-impact dataset:**  SELVABOX fills an important gap for tropical regions. The scale, diversity, and annotation detail are impressive, and the dataset is likely to become a strong reference for future research in this area.\n- **Practical evaluation metric:**  The proposed RF175 metric focuses on raster-level performance rather than patch-level mAP, which makes sense for real-world forestry applications.\n- **Comprehensive experiments:**  The authors benchmark various models, image resolutions, and training setups, providing clear evidence for each design choice.\n- **Good open-science practice:**  The dataset, preprocessing pipeline, and code are fully released, supporting reproducibility and future extensions.\n- **Clear and well-written paper:**  The paper is well organized, the figures are helpful, and the results are easy to interpret.", "weaknesses": "1. The paper does not report any **quantitative analysis of annotation consistency**. Although the annotation process is described in detail, we do not know how consistent different annotators were. Some inter-annotator agreement numbers (like IoU or Cohen’s kappa) would really help readers trust the data quality.\n2. The **test set completeness** is mentioned but not clearly verified. It would be good to show that the test areas were fully annotated or to quantify potential missing crowns. Without this, the reliability of RF175 as a “gold-standard” metric is slightly uncertain.\n3. The **RF175 metric** seems quite sensitive to the IoU threshold and NMS parameters, yet this sensitivity is not explored. It would be interesting to see how performance changes for IoU thresholds of 0.6 or 0.8.\n4. The **Detectree2 evaluation** may include unintentional data overlap because its original split cannot be reproduced. The authors mention this but still treat it as an “in-distribution” case, which could confuse readers. A clearer statement or a controlled subset would make the comparison more trustworthy.\n5. The **comparison with existing datasets** is mostly visual. Statistical comparisons, such as KL divergence or KS tests on crown-size distributions, would make the argument for SELVABOX’s uniqueness stronger.\n6. The **ethical and dual-use discussion** is too brief. The dataset could be misused, for example, for illegal deforestation or land exploitation. A short section on “Responsible Use” with clear terms or usage guidelines would be helpful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4u2xiq8w6r", "reviewer": "Reviewer_mwqm", "summary": "The paper introduces SELVABOX, a high-resolution dataset for tropical tree crown detection, featuring over 83,000 manually labeled crowns from drone imagery across Brazil, Ecuador, and Panama. It highlights the challenges of detecting individual tree crowns in tropical forests due to their variability and overlap, advocating for advanced remote sensing methods.", "questions": "# Questions:\n\n1. How does the masking strategy for incomplete annotations impact model training performance compared to datasets with full annotations?\n\n2. What are the specific challenges of scaling RGB-only methods to diverse tropical forest conditions not addressed by SELVABOX?\n\n3. How might the inclusion of LiDAR data enhance the detection accuracy of models trained on SELVABOX?\n\n4. What are the potential biases introduced by the manual annotation process conducted by a limited number of biologists?\n\n5. How does the zero-shot performance of SELVABOX-trained models compare to fine-tuned models on non-tropical datasets over a longer term?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "# Strengths:\n\n(+) The SELVABOX dataset's scale, with over 83,000 annotations, is an order of magnitude larger than existing tropical forest datasets, providing a substantial resource for training and validation.\n\n(+) Higher-resolution inputs consistently enhance detection accuracy, as demonstrated through extensive benchmarks, offering valuable insights for model optimization.\n\n(+) The open-access nature of the dataset, code, and pre-trained weights fosters collaboration and accelerates research in tropical forest monitoring.", "weaknesses": "# Weakness: \n\n(-) Incomplete annotations in some areas (e.g., Brazil and Ecuador rasters) may introduce noise and reduce model reliability, despite the masking strategy employed.\n\n(-) The reliance on RGB-only validation, due to logistical and cost constraints, may limit accuracy compared to LiDAR-based methods in complex tropical canopies.\n\n(-) The paper lacks a detailed comparison with LiDAR-based datasets, potentially overlooking a key alternative for tropical forest monitoring.\n\nMinor Suggestions:\n\nSome prior works of detection in forest scenarios are missing [1-5]. It would be better if the author could discuss them in the related work.\n\n**Ref:**\n\n[1] From drones to phenotype: using UAV-LiDAR to detect species and provenance variation in tree productivity and structure. Remote Sensing, 2020.\n\n[2] Explainable identification and mapping of trees using UAV RGB image and deep learning. Scientific Reports, 2021.\n\n[3] A general deep learning model for bird detection in high-resolution airborne imagery. Ecological Applications, 2022.\n\n[4] Benchmarking wild bird detection in complex forest scenes. Ecological Informatics, 2024.\n\n[5] Enhancing palm precision agriculture: An approach based on deep learning and UAVs for efficient palm tree detection. Ecological Informatics, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)