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
Det er ikke registrert sammendrag
Sammendrag
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.
Sammendrag
Accurately predicting whether pedestrians will cross in front of an autonomous vehicle is essential for ensuring safe and comfortable maneuvers. However, developing models for this task remains challenging due to the limited availability of diverse datasets containing both crossing (C) and non-crossing (NC) scenarios. Therefore, we propose a procedure that leverages synthetic videos with C/NC labels and an untrained model whose architecture is designed for C/NC prediction to automatically produce C/NC labels for a set of real-world videos. Thus, this procedure performs a synth-to-real unsupervised domain adaptation for C/NC prediction, so we term it S2R-UDA-CP. To assess the effectiveness of S2R-UDA-CP in self-labeling, we utilize two state-of-the-art models, PedGNN and ST-CrossingPose, and we rely on the publicly-available PedSynth dataset, which consists of synthetic videos with C/NC labels. Notably, once the real-world videos are self-labeled, they can be used to train models different from those used in S2R-UDA-CP. These models are designed to operate onboard a vehicle, whereas S2R-UDA-CP is an offline procedure. To evaluate the quality of the C/NC labels generated by S2R-UDA-CP, we also employ PedGraph+ (another literature referent) as it is not used in S2R-UDA-CP. Overall, the results show that training models to predict C/NC using videos labeled by S2R-UDA-CP achieves performance even better than models trained on human-labeled data. Our study also highlights different discrepancies between automatic and human labeling. To the best of our knowledge, this is the first study to evaluate synth-to-real self-labeling for C/NC prediction.
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
SmartForest will position the Norwegian forest sector at the forefront of digitalization resulting in large efficiency gains in the forest sector, increased production, reduced environmental impacts, and significant climate benefits. SmartForest will result in a series of innovations and be the catalyst for an internationally competitive forest-tech sector in Norway. The fundamental components for achieving this are in place; a unified and committed forest sector, a leading R&D environment, and a series of progressive data and technology companies.