DigiForests: A Longitudinal LiDAR Dataset for Forestry Robotics

“DigiForests” is a real-world, longitudinal dataset for forestry robotics that enables the development and comparison of approaches for various relevant applications, ranging from semantic interpretation to estimating traits relevant to forestry management. The dataset consists of multiple recordings of the same plots in a forest in Switzerland during three different growth periods. We recorded the data with a mobile 3D LiDAR scanning setup. Additionally, we provide semantic annotations of trees, shrubs, and ground, instance-level annotations of trees, as well as more fine-grained annotations of tree stems and crowns. Furthermore, we provide reference field measurements of traits relevant to forestry management for a subset of the trees.

For further information and downloading the data, please visit our dataset webpage.

Oxford Forest Place Recognition Dataset

A second supplementary dataset focusing on the problem of LiDAR-based Place Recognition has also been released to connect with the following publication:

“Evaluation and Deployment of LiDAR-based Place Recognition in Dense Forests”
Presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024 Authors: Haedam Oh, Nived Chebrolu, Matias Mattamala, Leonard Freißmuth and Maurice Fallon

The dataset can be downloaded from it own webpage.