Maciej Wielgosz
Research Scientist
Biography
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.
Authors
Stefano Puliti Binbin Xiang Maciej Wielgosz Eivind Handegard Nicolas Cattaneo Marta Vergarechea Terje Gobakken Juha Hyyppä Erik Næsset Mikko Vastaranta Tuomas Yrttimaa Rasmus AstrupAbstract
Accurately determining the age of individual trees is important for understanding forest dynamics, tree growth, site productivity and describing ecological processes. Traditional methods, such as dendrochronological coring, are invasive, labor-intensive, and costly. This study investigates the use of deep learning (DL) to predict tree age from high-density laser scanning data as a scalable, non-invasive alternative. The dataset includes approximately 1700 tree point clouds from approx. 1 K trees across Norway, Sweden, and Finland, encompassing Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) and a broad range of tree age and developmental stages, from young seedlings (1 year) to old trees (∼350 years). Data were collected using terrestrial, mobile, and high-density airborne laser scanning platforms, enabling the development of sensor-agnostic models. We evaluated multiple modelling approaches, from linear regression to transformer architectures, using both training-from-scratch and fine-tuning strategies. Models fine-tuned starting from pre-trained weights from ForestFormer3D's U-Net as well as the transformer architecture (PointTransformerV3) trained from scratch, proved effective for age regression (RMSE ≤23 years). Although our analysis was limited to two tree species, we demonstrated that a single joint age-estimation model can be successfully trained for both species. We demonstrate that models trained on high-resolution data can generalize to lower-resolution, less costly inputs, provided that data augmentations that mimic reduced resolutions are included during training. This study presents a data-driven framework for estimating tree age without destructive sampling. The findings support the potential for AI-based methods to complement or replace traditional age estimation techniques in forest inventory and monitoring.
Abstract
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Abstract
NIBIOs eksperter på skog og kunstig intelligens (KI) trener opp datamodeller til å kjenne igjen enkelttrær i skogen. Utgangspunktet er data fra laserskanning. Jobben er enorm. Målet: Å gå fra bestandsskogbruk til forvaltning av skog på enkelttrenivå.
