A multi-sensor multi-resolution dataset to support forest inventory methods

Abstract. Accurate estimation of forest structural and taxonomic parameters is vital for biodiversity monitoring, carbon accounting and sustainable management. Most of the current methods for estimating these parameters are still developed and tested on site-specific case studies, limiting reproducibility and cross-site generalization. This paper introduces 3D3, a multi-sensor and multi-resolution benchmark dataset designed to evaluate 3D forestry algorithms across diverse European forest types. 3D3 includes data collected by airborne, helicopter, UAV and terrestrial (static and mobile) laser scanning systems along with RGB and hyperspectral imagery, covering a variety of forest types (Boreal, Alpine and Mediterranean). By encompassing both mono- and multi-wavelength laser data, 3D3 represents a unique resource for developing new algorithms and evaluating them on distinct datasets. Each site provides ground truth for at least one task among Individual Tree Segmentation (ITS), Forest Semantic Segmentation (FSS) or tree parameter estimation and species classification.