Conference Agenda

The Terrasolid software suite provides an automated and scalable framework for large-scale LiDAR data processing, widely adopted in both national and private forest inventories. Its unified processing pipeline covers all essential steps—from point cloud import and georeferencing to ground classification, object detection, tree segmentation, and computation of individual-tree attributes such as diameter at breast height (DBH), height, volume, and tree species. Georeferencing is initially performed in TerraScan using signal markers or automatically detected tree trunks, with optional refinement in TerraMatch, which corrects angular misalignments between flight lines.

Following object classification, individual trees are extracted from points labeled as trees. The semi-manual Group Inspection tools support efficient correction of segmentation errors, such as merged or over-segmented trees, after which stem-wise metrics are automatically updated. These conventional modules rely on optimized algorithms capable of processing hundreds of millions of points within minutes.

A recent innovation, the Tree Species tool, introduces one of the first AI-based extensions within Terrasolid software. It employs a machine learning approach that integrates 2D raster-based features with 3D point cloud descriptors to achieve accurate tree species identification. Validation was conducted using the FOR-species20K dataset, comprising 33 species collected worldwide. Among several tested classifiers, the Histogram Gradient Boosting Classifier (HGBC) achieved the highest accuracy. To mitigate class imbalance, multiple side-view rasterizations and SVM-SMOTE oversampling were applied, significantly improving the separability of underrepresented species and overall classification robustness.

Traditional aboveground biomass estimation for forested areas relies on allometric equations (Návar, 2009), which use input variables such as diameter at breast height (DBH), tree height, and tree species or broader taxonomic group. Although allometric equations can estimate the biomass of individual trees, and stand-level equations exist for larger scales, they often require extensive field data, making them less suitable for densely clustered or remote forests. However, satellite images provide increasingly detailed global observations of forested areas, and spaceborne lidar data like GEDI (Duncanson et al., 2022) provide accurate measurements for canopy height across different ecozones worldwide. In recent years, foundation models (FMs) inspired by large language models (Vaswani et al., 2017) have become the new paradigm to leverage large amounts of unlabelled data through self-supervised pre-training and have shown capacity to benefit multiple downstream tasks. In this work, we adopt the Granite foundation model (Muszynski et al., 2024) as a baseline to improve aboveground biomass estimation on different satellite data, using the Mixedwood Plains Ecozone (MPE) as a case study. We also explore adding temporal, geospatial, and spatiotemporal features and validate the proposed spatiotemporal foundation model with field sampling plots.

Accurate detection and mapping of individual trees and their species are vital for sustainable forest management. Traditional field-based inventories remain the golden standard in forest monitoring, but are increasingly overwhelmed by temporal, spatial and accessibility constraints. Remote sensing offers faster, repeatable, and high-resolution data that complement and scale beyond field inventories. However, species-level detection remains difficult due to overlapping crowns, and spatial mismatches between crowns and trunks. Deep learning (DL) methods, particularly convolutional neural networks, have advanced crown delineation by automatically learning spatial and spectral patterns from imagery. Yet, their success depends heavily on dataset quality, class balance, and diversity. To address this, we applied a DL object detection framework for tree crown and species detection in Swiss forests and evaluate how dataset composition and training strategies influence accuracy and generalization. We test three dataset configurations: (1) an unbalanced masked dataset, (2) a class-balanced masked dataset, and (3) a mixed dataset combining masked and unmasked imagery. Results show that class balancing improved accuracy for both dominant and minority species, while mixed data enhances generalization.

High-resolution airborne sensing enables joint mapping of urban infrastructure and forest composition at ecological scales. This study presents a self-supervised segmentation framework that fuses 0.5 m Near-Infrared + RGB (NIRGB) orthophotography from the Ontario Imagery Program (2013–2026) with Canopy-height models (CHM) derived from the Ontario Elevation Mapping Program (8–10 pulses m⁻², 5–10 cm vertical accuracy). Imagery was collected during the leaf-off season, providing strong spectral–structural contrast between evergreen and deciduous crowns, to produce high-fidelity land- cover segmentations that differentiate vegetation functional types and built structures as a prerequisite for tree-level biomass and carbon-stock estimation.

Spatially accurate forestry road networks are essential for effective forestry operations, sustainable resource management, and conservation. Current forestry road databases in Ontario have significant location errors due to limitations and human errors associated with conventional road delineation approaches such as GPS-based field surveys and photointerpretation. A previously developed algorithm, which used airborne laser scanning (ALS) data, successfully corrected road locations in Quebec. However, its design limited its application in other landscapes, ALS instruments, and road construction and maintenance practices. This study advances that algorithm by integrating a deep learning component to improve its robustness and scalability for diverse forest conditions. A hybrid workflow combines the original friction-based conductivity surface with a road probability surface generated by an Attention Residual U-Net model trained on 11 LiDAR-derived features using road segments from five forest sites in Quebec. The enhanced workflow was applied to two forest management units in Ontario: Nipissing and Dryden. The results showed significant improvement in road alignment when compared to the existing provincial data and the outputs from the earlier automated approach. The deep learning-enhanced algorithm lowered mean positional error by 78% (from 9.36 m to 2.07 m) and increased the proportion of road centerline points within 3 m of the reference from 66.7% to 87.2%. These improved centerline accuracies will further support a scalable tool for rapid and accurate forestry road network mapping, which in turn will aid sustainable forest management and conservation planning at both provincial and national scales.

Monitoring tree health is essential for detecting early signs of stress, defoliation, and potential mortality, supporting effective forest management, ecosystem conservation, and early warning systems. Advances in deep learning have enabled automated analysis of trees in remote sensing imagery through object detection methods that leverage both spectral and spatial information. However, assessing tree defoliation remains challenging, as subtle differences between defoliation levels make accurate classification difficult. To address this, we propose the hybrid ResNet-Swin Transformer, an object detection architecture built on a Faster R-CNN framework, incorporating a fused ResNet and Swin Transformer backbone with attention-based feature fusion. This design captures rich, multiscale representations by combining convolutional and transformer-based features and progressively refines them through channel-wise attention blocks for robust detection and classification. The architecture was evaluated on a very high-resolution aerial dataset from Switzerland, partially annotated with five classes: Conifer (healthy), Conifer (defoliated), Broadleaf (healthy), Broadleaf (defoliated) and Dead. Comparative experiments with state-of-the-art object detection and classification methods demonstrate that the proposed approach achieves higher accuracy and robustness, highlighting its potential for precise and reliable automated tree health monitoring.

Accurate vegetation mapping in complex urban environments is essential for ecological monitoring, biodiversity assessment, and sustainable park management. However, fine-grained vegetation segmentation remains challenging because of the high diversity of plant species, overlapping canopies, and the interference of artificial objects. To address these challenges, a deeply supervised parallel architecture based on the SegFormer backbone was proposed in this paper. The model incorporated a SegFormer-ASPP-low-level (SAL) head, which fused high-level semantic representations, multi-scale contextual information, and low-level spatial details through a parallel decoding mechanism. Two auxiliary heads, a pyramid pooling module (PSP) and a fully convolutional network (FCN), were added to provide deep supervision and improve the recognition of blurred boundaries and rare categories. High-resolution UAV imagery was used to perform fine-grained semantic segmentation of 17 vegetation categories. The dataset included multiple tree species as well as non-tree classes such as Nelumbo sp. (lotus) and dead trees. Experimental results showed that our model achieved a mean intersection over union (mIoU) of 73.57%, outperforming architectures such as SegFormer-b1, DeepLab v3+, ConvNeXt and SCTNet. Visual analysis further demonstrated the model's robustness in complex urban park scenes, showing superior boundary delineation, improved recognition of small and spectrally similar species, and resilience to interference from artificial objects like plastic lawns and landscape lighting. The proposed approach offers valuable insights for precision forestry, ecological monitoring, and intelligent UAV-based remote sensing applications.