Important results from the project
The project has largely delivered the planned results and the set goals have been achieved. An iterative work process in close dialogue with the needs owners made it possible to adjust the efforts along the way. For example, we opted out of a planned noble leaf analysis and instead focused on trees with high biological value. We also refrained from training a GAN model as it was not considered to provide sufficient benefit for the project.
Expected long term effects
The results can ultimately support the Swedish forest industry in developing models for the analysis of ecological disturbances more quickly, which increases preparedness for climate challenges. The project also strengthens Sweden´s competitiveness in AI-based tree analysis, with potential to export this expertise to forest regions in the northern coniferous forest belt. The project has also deepened our networks and established new collaborations.
Approach and implementation
The project followed an iterative work process that allowed for both research and development. Thanks to this approach, we were able to be curious, test different methods, and adjust the focus as new insights emerged. The project was completed on schedule, without major deviations, and the collaboration between participating parties worked well. The iterative approach contributed to the implementation of the right activities and enabled an effective exploration of the project´s target area.
External links
The project description has been provided by the project members themselves and the text has not been looked at by our editors.
