## 3 Results
**(Row 3, Column 2)** Structural HSPC component of HSD-L3D prediction. **(Row 4, Column 2)** Lidar point cloud component HSD-L3D prediction. side of the test area (top left subplot of Figure 8), which is subjectively incorrectly labeled entirely as the building class, was best segmented by this model (which in turn negatively affected its reported accuracy). The moderate increase in balanced and pixel accuracy improvements of the H3D-DSM network over the multimodal network is mainly attributed to the greatly increased training cost of the multimodal network. The H3D-DSM, and all other unimodal networks, are smaller architectures in terms of both number of parameters and input point cloud size. As a result, they are faster to train and can be fed much larger batch sizes. This allows for a greater number of epochs and sample variety per unit of computational cost. Further, the structural HSPC itself is already a multimodal data product and provides a subjectively reasonable and pre-defined association of hyperspectral and lidar features in the spatial domain. As a result, this model may have been able to rely on this fact, whereas the multimodal network had to learn a useful mapping between its input unimodal feature sets. The combined and projected (Section 2.2.5) test set predictions for all networks are provided in Figure 8. The metrics and statistics from these predictions are provided in Table 4. This table further provides the results of the H2D unimodal traditional pixel CNN network ingesting hyperspectral images and the H2D_L2D multimodal traditional pixel CNN network ingesting hyperspectral images and lidar DSM data originally described in [PERSON] et al. (2018). In the figure and table, all predictions are directly comparable because they are all localized to the same label pixel locations. Note, the metrics and statistics of the HSPC ingesting models did not change because of the bijective mapping between the HSPC point locations and label pixel locations. We also comment on the overall decrease in the reported statistics for non-HSPC-based models (L3D, HSD-L3D (Lidar), and HSD-L3D (Canonical)). This is a result of the in-exacting process of projecting and interpolating the predicted labels localized to their respective point locations to a regularly gridded and smaller set of pixel locations. The largest inaccuracy is in the selection of a label from a \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **L3D** & **HSD-L3D (Lidar)** & **H3D-Flat** & **H3D-DSM** & **HSD-L3D (HSPC)** & **HSD-L3D (Canonical)** \\ \hline
**Point Locations** & **Lidar** & **Lidar** & **HSPC** & **HSPC** & **HSPC** & **Canonical** \\ \hline Class Accuracy & & & & & & \\ building (57.4\%) & **0.996** & 0.958 & 0.735 & **0.960** & **0.960** & 0.958 \\ vehicle path (15.7\%) & 0.861 & **0.896** & 0.861 & **0.952** & 0.908 & 0.897 \\ human path (17.9\%) & **0.528** & 0.431 & 0.271 & 0.386 & **0.415** & 0.430 \\ foliage (6.6\%) & 0.413 & **0.635** & **0.911** & 0.723 & 0.550 & 0.629 \\ vehicle (2.4\%) & **0.928** & 0.841 & 0.241 & 0.767 & **0.796** & 0.837 \\ \hline Statistics & & & & & & \\ Precision & **0.8218** & 0.7636 & 0.6881 & **0.8396** & 0.7679 & 0.7640 \\ Recall & 0.7451 & **0.7520** & 0.6038 & **0.7579** & 0.7256 & 0.7500 \\ F-measure & **0.7632** & 0.7461 & 0.6009 & **0.7848** & 0.7317 & 0.7452 \\ IoU & **0.6461** & 0.6135 & 0.4676 & **0.6695** & 0.5960 & 0.6123 \\ Kappa & 0.7060 & **0.7478** & 0.6017 & **0.7875** & 0.7252 & 0.7462 \\ \hline Balanced Accuracy & 0.7451 & **0.7520** & 0.6038 & **0.7579** & 0.7256 & 0.7500 \\ Pixel Accuracy & 0.8360 & **0.8536** & 0.7442 & **0.8744** & 0.8385 & 0.8525 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics and metrics of combined test sample predictions for all models. Note, predictions are not directly comparable unless they were performed against the same set of point locations. For example, the L3D and HSD-L3D (Lidar) columns are comparable, while the L3D and H3D-Flat predictions are not.