My expertise lies in the field of machine learning; I'm dealing with Point Cloud segmentation models. My primary focus is to address the challenges posed by sparse sections of point clouds, especially those that are crucial for forestry applications, like the sections near the base of tree trunks. While data from drones and airplanes is readily accessible, ensuring high semantic accuracy during processing can be quite intricate. Therefore, new methods in point cloud instance and semantic segmentation are needed.

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The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes. In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file. A full description of the FOR-instance data can be found at

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The FOR-instance dataset (available at this https URL) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.


Inthis study, we introduce Point2Tree, a modular and versatile framework that employs a three-tiered methodology, inclusive of semantic segmentation, instance segmentation, and hyperparameter optimization analysis, designed to process laser point clouds in forestry. The semantic segmentation stage is built upon the Pointnet++ architecture and is primarily tasked with categorizing each point in the point cloud into meaningful groups or ’segments’, specifically in this context, differentiating between diverse tree parts, i.e., vegetation, stems, and coarse woody debris. The category for the ground is also provided. Semantic segmentation achieved an F1-score of 0.92, showing a high level of accuracy in classifying forest elements. In the instance segmentation stage, we further refine this process by identifying each tree as a unique entity. This process, which uses a graph-based approach, yielded an F1-score of approximately 0.6, signifying reasonable performance in delineating individual trees. The third stage involves a hyperparameter optimization analysis, conducted through a Bayesian strategy, which led to performance improvement of the overall framework by around four percentage points. Point2Tree was tested on two datasets, one from a managed boreal coniferous forest in Våler, Norway, with 16 plots chosen to cover a range of forest conditions. The modular design of the framework allows it to handle diverse pointcloud densities and types of terrestrial laser scanning data.