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FORWARD: Dataset of a forwarder operating in rough terrain.

Mikael Lundbäck1, Erik Wallin1, Carola Häggström2

  • 1Umeå University, Department of Physics, Umeå, SE-90187, Sweden.

Data in Brief
|April 21, 2026
PubMed
Summary

We introduce FORWARD, a multimodal dataset for developing AI in forestry. This high-resolution data captures a forwarder in rough terrain, aiding research in autonomous control and trafficability.

Keywords:
Cut-to-length harvestingField roboticsForestryForestry automationMachine learningModeling and simulationOffroad vehiclesTerrain traversability

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Area of Science:

  • Forestry Science
  • Robotics
  • Computer Vision

Background:

  • Autonomous operation of forest machinery is crucial for efficiency and safety.
  • High-resolution multimodal datasets are needed for developing advanced AI algorithms in this domain.

Purpose of the Study:

  • To present the FORWARD dataset, a comprehensive collection of sensor data from a forestry forwarder.
  • To facilitate the development of AI models for trafficability, perception, and autonomous control of forest machines.

Main Methods:

  • Collected high-resolution multimodal data from a Komatsu forwarder using telematics, cameras, IMUs, and operator sensors.
  • Annotated 18 hours of 360° video data with work elements and included StanForD production logs.
  • Conducted experiments with varying conditions (steel tracks, load weights, speeds) on forest roads and terrain.

Main Results:

  • The FORWARD dataset provides centimeter-accurate positioning, detailed sensor logs, and extensive video material.
  • Includes annotated work elements and scenario specifications for diverse research applications.
  • Data is made publicly available for long-term use in research and development.

Conclusions:

  • The FORWARD dataset is a valuable resource for advancing AI in forestry, particularly for autonomous control, perception, and trafficability.
  • Enables research into efficiency, fuel consumption, safety, and environmental impact of forest operations.
  • Supports the auto-generation and calibration of forestry machine simulators and automation scenarios.