SCA uses AI for digital forestry planningFriday 27 Sep 2024
The goal is that by 2025, all forest planners at SCA Forest will use digital forestry planning to prepare for harvesting in SCA’s own forests. “Digital forestry site planning brings several positive aspects. The most important is that we achieve more efficient forestry site planning thanks to higher and more consistent quality of our forest data, and a large part of the planning work can be done in the office. Additionally, we can plan more during the winter,” says Magnus. In-House Developed Tool Behind the initiative for digital forestry planning lies extensive development work by SCA Forest’s technology and digitisation staff. By utilizing information from forest laser scanning and harvester data in combination with AI, SCA has created a digital twin of the forest using automated data interpretation. “We have developed our own tool that provides our forestry planners with access to high-resolution images from different perspectives and data about the forest, so they can look at the forest and analyse the area,” says Magnus. “This means that forestry planners always plan in the office and verify different parts in the field when needed. The tool contains a wealth of data about the forest and each individual tree. It becomes a great support for the planners,” explains Per Östman from Forest and Management, responsible for implementing the new tool in the operations. “It will also provide better data for our Production function,” adds Magnus. The new tool is fully integrated with the “TP application”, another tool developed by SCA for forestry planning. “Our new tool is like a viewing window for interpreting images and can only be used online and works best in the office, while the TP app is where the actual forestry planning work takes place and also works offline and thus out in the forest,” says Magnus. With digital forestry planning, the planners can focus on visiting more complex forest areas that need to be visited in the field. More >> Source & image credit: SCA | ||
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