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Publications

NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.

2023

Authors

Cristina Micheloni Frank Willem Oudshoorn María Isabel Blanco Penedo Sari Autio Andrea Beste Jacopo Goracci Matthias Koesling Ursula Kretzschmar Eligio Malusá Bernhard Speiser Jan van der Blom Felix Wäckers

Abstract

No abstract has been registered

Authors

Silje Maria Midthjell Høydal Bjørn Arild Hatteland Joseph Chipperfield John Arvid Grytnes

Abstract

No abstract has been registered

Authors

Hannes Stolze Kristina Bringedal Gedde Holger Militz

Abstract

No abstract has been registered

To document

Authors

Jana Fránová Jaroslava Pribylová Rostislav Zemek Jiunn Luh Tan Zhibo Hamborg Dag-Ragnar Blystad Ondrej Lenz Igor Koloniuk

Abstract

No abstract has been registered

To document

Authors

Helge Berglann

Abstract

No abstract has been registered

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Authors

Robert Jankowiak Natalia Gumulak Piotr Bilański Halvor Solheim Marek Tomalak Michael J. Wingfield

Abstract

No abstract has been registered

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Authors

Trond Haraldsen

Abstract

No abstract has been registered

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Authors

Marianne Stenrød Kathinka Lang Marit Almvik Roger Holten Agnethe Christiansen Xingang Liu Qiu Jing

Abstract

No abstract has been registered

Authors

Divina Gracia P. Rodriguez

Abstract

No abstract has been registered

See dataset

Authors

Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus Astrup

Abstract

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 http://arxiv.org/abs/2309.01279