Research infrastructure The Computational Forestry Lab (CFL) is a specialized super computing facility for digital forestry research. It enables high-performance computing with large amounts of data from airborne laser scanning, forest machines, and advanced simulations. The infrastructure supports both academic and industry users.
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The Computational Forestry Lab (CFL) is a research infrastructure at Umeå University, designed to accelerate the digital transformation of forestry through high-performance computing and adaptation of artificial intelligence. Powered by a compact but powerful supercomputer, CFL enables advanced analysis and modelling of large datasets from sources such as airborne laser scanning, sensor-equipped forest machinery, and computer simulations.
This infrastructure is uniquely tailored for forestry research, addressing the need for computational capacity for sustainable precision forestry through digitalization and automation. It supports cooperation across subject- and company-academy boundaries with shared and partially confidential data, models and tools. This lends itself to a platform for testing new algorithms and models directly within the system—eliminating the need for reimplementation or data transfer.
CFL is free and available to researchers and external partners who work on forestry related questions, either requiring storage, preprocessing power or AI-training capabilities. More detailed information on the system and how to contact us is found on our website.
CFL features a dual-interface system: one with guided workflows for newcomers to high-performance computing, and one for experienced users with full control over the computational tasks. The lab is supported by funding from the Kempe Foundation and Mistra Digital Forest, and is physically hosted at HPC2N, Umeå University's supercomputing centre. The primary users are researchers in Mistra Digital Forest at the Swedish University of Agricultural Sciences, Skogforsk, and Umeå University. However, the resource will be widely accessible as much as the capacity allows.
