Forest inventory is constantly looking to increase the precision and accuracy of the measured attributes while reducing measurements costs. A plethora of the tools can be used for measurements, out of which remote sensing provides the most cost-efficient way of estimating forest resources for large areas. Among the remote sensing data used for forest inventory, point clouds seems to be most suited for dimensional estimates, as they have a 3D structure. The strength and weakness of the point clouds used in forestry is the large amount of data describing the vegetation and the ground. The strength lies on the wall-to-wall coverage of the forest ecosystem, which is also its weakness, as most estimates are based on algorithms, that naturally are erroneous. Modern forest inventory moved from stand level attributes to comprehensive description of individual trees from a stand. To capture the vertical dimension of each tree laser scans are currently used. The project will use laser scanner installed on unmanned aerial systems (UAS) and on handheld systems (HLS) to describe the forest from below and above the canopy. Using fused terrestrial and aerial point clouds, a forest inventory will be executed using deep learning algorithms training using either point clouds or rasters (images) created from point clouds. The project has three objectives: 1. Convert raw data recorded by the sensors into a usable file format 2. Fuse aerial and terrestrial point clouds 3. Create the input for the deep learning algorithms by segmenting point clouds and rasters