DigiForest: Digital Analytics and Robotics for Sustainable Forestry

2026-04-16

AI Summary

Traditional forest management struggles to simultaneously meet the European Union’s climate neutrality and biodiversity objectives. This work proposes a large-scale precision forestry system that, for the first time, integrates heterogeneous autonomous robots—including aerial, legged, and wheeled platforms—with computer vision–driven automated extraction of tree traits, forest growth prediction models, and a decision support system. The project also develops low-impact selective harvesting equipment to enable end-to-end digitalization and autonomous operations. Empirical trials in Finland, the United Kingdom, and Switzerland demonstrate that the system efficiently performs high-precision forest inventory, provides intelligent decision support, and enables ecologically sensitive automated harvesting, thereby advancing forestry toward a sustainable, intelligent paradigm.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Covering one third of Earth's land surface, forests are vital to global biodiversity, climate regulation, and human well-being. In Europe, forests and woodlands reach approximately 40% of land area, and the forestry sector is central to achieving the EU's climate neutrality and biodiversity goals; these emphasize sustainable forest management, increased use of long-lived wood products, and resilient forest ecosystems. To meet these goals and properly address their inherent challenges, current practices require further innovation. This chapter introduces DigiForest, a novel, large-scale precision forestry approach leveraging digital technologies and autonomous robotics. DigiForest is structured around four main components: (1) autonomous, heterogeneous mobile robots (aerial, legged, and marsupial) for tree-level data collection; (2) automated extraction of tree traits to build forest inventories; (3) a Decision Support System (DSS) for forecasting forest growth and supporting decision-making; and (4) low-impact selective logging using purpose-built autonomous harvesters. These technologies have been extensively validated in real-world conditions in several locations, including forests in Finland, the UK, and Switzerland.

Problem

• sustainable forestry
• climate neutrality
• biodiversity
• forest management
• resilient ecosystems

Innovation

• precision forestry
• autonomous robotics
• forest inventory
• decision support system
• selective logging