Maciej Wielgosz
Forsker
Biografi
Min ekspertise ligger innenfor feltet maskinlæring; jeg jobber med segmenteringsmodeller for punktskyer. Mitt hovedfokus er å håndtere utfordringene som stilles av sparsomme deler av punktskyer, spesielt de som er avgjørende for skogbruksapplikasjoner, som seksjonene nær bunnen av trestammer. Mens data fra droner og fly er lett tilgjengelige, kan det å sikre høy semantisk nøyaktighet under behandling være ganske innviklet. Derfor er det behov for nye metoder i instans- og semantisk segmentering av punktskyer.
Sammendrag
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Forfattere
Binbin Xiang Maciej Wielgosz Theodora Kontogianni Torben Peters Stefano Puliti Rasmus Astrup Konrad SchindlerSammendrag
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.
Sammendrag
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Divisjon for skog og utmark
Automating Biodiversity Mapping: AI Applications in the Identification of Forest Habitats
I dette prosjektet skal vi undersøke hvordan KI og smarttelefoner kan brukes ved kartlegging av viktige skogstrukturer, og komme forbi begrensninger med tradisjonelt feltarbeid.
Divisjon for skog og utmark
SFI SmartForest: Bringing Industry 4.0 to the Norwegian forest sector
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