## 3 Results
A caveat to this is that all HSPC predictions are directly comparable; they share the same set of point locations albeit with different \(Z\) values. Further note, as described in Section 2.2.1, L3D was originally described in (2018) but retrained in this work provided it is a sub-part of the HSD-L3D network. The best results per point location type are highlighted in bold. The class distribution percentage within the test set is provided beside each class label. set of labels that fall within a single pixel location. This phenomenon is closely related to issues with generating the lidar point cloud labels in the first place (Section 2.1.1). First, we compare the results of the unimodal network predictions L3D, H3D-Flat, H3D-DSM, and H2D [8]; note that Table 4 uses the shorthand naming for H3D-Flat (HSF) and H3D-DSM (HSD). In terms of both balanced and pixel accuracy, the HSD network outperforms all other unimodal networks. We attribute this to the same reasoning presented for H3D-DSM's performance when reviewing the unprojected predictions, the structural HSPC itself is already a multimodal data product and provides a subjectively reasonable and pre-defined association of hyperspectral and lidar features. Further, all other unimodal networks have no source of multimodal information. The structural HSPC provides some structural information by definition, and thus provides multimodal information. If we then compare all other unimodal networks, we find that the H2D [8] outperforms all others in terms of balanced and pixel accuracy. H2D weakly outperforms L3D, from this, we have moderate evidence that the amount of predictive information contained within the hyperspectral and lidar modalities are approximately equal. This is further evidence and expounded upon by comparing the per-class accuracies of the L3D and H2D networks to all other results. We find that the L3D and H2D networks collectively contain the highest number of best results for per-class accuracies. As may be assumed the L3D network ranks highest for more structurally defined classes like the building and vehicle class, while the H2D network ranks highest for the more spectrally defined classes human path and foliage. Broadening the scope of the comparison to include the decomposed HSD-L3D lidar and hyperspectral components, we find the same performance rankings hold. These results  
Figure 8: Combined test sample predictions over the entire test set area for all models. Note, all predictions have been projected into the \(XY\) plane and localized to the set of pixel locations of the pixel labels as described in Section 2.2.5. As a result, all predictions are directly comparable. **(Row 1, Column 1)** False color hyperspectral image of the labeled test set area. **(Row 1, Column 2)** Ground truth pixel labels. **(Row 2, Column 1)** L3D prediction. **(Row 3, Column 1)** H3D-Flat prediction. **(Row 4, Column 1)** H3D-DSM prediction. **(Row 2, Column 2)** HSD-L3D prediction. **(Row 3, Column 2)** Structural HSPC component of HSD-L3D prediction. **(Row 4, Column 2)** Lidar point cloud component HSD-L3D prediction. also present further evidence of the approximately equal predictive power of the two modalities. This can be seen in the closely matched balanced and pixel accuracy results of the HSD-L3D (Lidar) and HSD-L3D (HSPC) models. Had one modality provided richer information from which the segmentations could be predicted, then we would expect the contribution to the overall performance from the components of a multimodal model like HSD-L3D to be further separated. A holistic review of the multimodal network performance incorporates the results of the H2D_L2D and H2D_L3D from [PERSON] et al. With these results, we find that the H2D_L2D network moderately outperformed all other multimodal networks described here and within [PERSON] et al. We attribute this result to the overall difficulty in creating and training mixed modality fusion-based neural networks specifically fusing dissimilar modalities and the computational cost of training KPConv-based networks.