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Digital Analytics and Robotics for Sustainable Forestry

Project description

Robots and AI for forest monitoring

Forests are essential for combatting climate change and maintaining our planet, with forestry operators and organisations monitoring forests, assisting their growth, limber output and maintenance. Unfortunately, monitoring large expanses of forest can be difficult and inaccurate which is dangerous for properly maintaining a forest's health. The EU-funded DIGIFOREST project introduces an innovative solution, improved forest monitoring and data management using robots and artificial intelligence. By utilising several different robots to collect 3D spatial data on forests and individual trees followed by AI documenting and AI representing it, it offers forestry operators and policymakers an unparalleled tool.


What if we could create a revolution in spatial data acquisition, organization and analysis and give forestry operators and enterprises up-to-date, tangible information about the status of their forests down to the individual tree? We believe this would improve their oversight by allowing more accurate growth modelling of forest stands and precise predictions of timber yields. It would remove the uncertainty of when thinning operations are needed or where there are trees which are ready for harvest. It could also enable operators to automatically plan where their staff or equipment should be deployed. With capable (semi-)autonomous harvesting, operators eventually automating the full process.

It could also better quantify a forest's carbon sequestration - with low uncertainty per-tree carbon estimates. Precise measures of crown volume and tree diameters would improve the granularity of carbon credit schemes. This could inform national governments and policy makers when deciding policy on initiatives such as carbon offsets and carbon farming.

In DIGIFOREST we propose to create such an ecosystem by developing a team of heterogeneous robots to collect and update this raw 3D spatial representations, building large scale forest maps and feeding them to machine learning and spatial AI to semantically segment and label the trees and also the terrain. Our robot team will be diverse: we will use both rugged field robots as well as more experimental vehicles. Most ambitious of all is the intention to (semi-)automate a lightweight harvester for sustainable selective logging.

Progress in this project will be demonstrated with an ambitious series of field trials. With the clear engagement of forestry and industrial companies, commercial pathways are readily available.

A 1:15 video summarizing the overall project ambitions and consortium can be viewed here:


Net EU contribution
€ 655 276,23
Arcisstrasse 21
80333 Muenchen

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Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
Total cost
€ 655 276,23

Participants (4)

Partners (5)