  The FOR-instance dataset (available at
https://doi.org/10.5281/zenodo.8287792) addresses the challenge of accurate
individual tree segmentation from laser scanning data, crucial for
understanding forest ecosystems and sustainable management. Despite the growing
need for detailed tree data, automating segmentation and tracking scientific
progress remains difficult. Existing methodologies often overfit small datasets
and lack comparability, limiting their applicability. Amid the progress
triggered by the emergence of deep learning methodologies, standardized
benchmarking assumes paramount importance in these research domains. This data
paper introduces a benchmarking dataset for dense airborne laser scanning data,
aimed at advancing instance and semantic segmentation techniques and promoting
progress in 3D forest scene segmentation. The FOR-instance dataset comprises
five curated and ML-ready UAV-based laser scanning data collections from
diverse global locations, representing various forest types. The laser scanning
data were manually annotated into individual trees (instances) and different
semantic classes (e.g. stem, woody branches, live branches, terrain, low
vegetation). The dataset is divided into development and test subsets, enabling
method advancement and evaluation, with specific guidelines for utilization. It
supports instance and semantic segmentation, offering adaptability to deep
learning frameworks and diverse segmentation strategies, while the inclusion of
diameter at breast height data expands its utility to the measurement of a
classic tree variable. In conclusion, the FOR-instance dataset contributes to
filling a gap in the 3D forest research, enhancing the development and
benchmarking of segmentation algorithms for dense airborne laser scanning data.
