## 5 Discussion of Results
### Novelties Introduced
#### 5.2.2 Resource Level  
EfficientNet-B0-based streams architecture led to remarkable achievements not only at the application level but also at a purely computational level. By substituting the VGG16-like encoders in the HAFNet model, the number of parameters shrunk dramatically from 88.978 M to 6.982 M. This size reduction brought multiple benefits that make the HAFNetE model production-ready:  
* Reduction of training time: the number of weights in a network is directly correlated with the number of gradients updates that the GPU needs to operate to optimize the loss function. A 92% parameters reduction coupled with an extra pre-trained stream translates to a 80% reduction in training time to reach the same model performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **F1 Score** \\ \hline DeepLab v3 + [35] & 95.8\% \\ MANet [36] & 95.91\% \\ DSMNet [37] & 96.0\% \\ DP-DCN [38] & 95.36\% \\ REMSNet [39] & 96.17\% \\ MMAFNet [40] & 96.26\% \\
**HAFNetE** & 96.68\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1-scores for state-of-the-art methods on the Potsdam dataset (building).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **IoU** & **F1-Score** & **Accuracy** \\ \hline HAFNet [8] & 90.10\% & **98.78\%** & 97.96\% \\ HAFNetE & **93.64\%** & 96.68\% & 97.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: HAFNetE and HAFNet performance comparison.  
Figure 7: Two examples of results obtained on two entire 6000 \(\times\) 6000 pixel tiles from the Potsdam dataset. (**a**) RGB TOP image of Tile 1. (**b**) HAFNetE classification result on Tile 1. (**c**) Ground Truth map for Tile 1. (**d**) RGB TOP image of Tile 2. (**e**) HAFNetE classification result on Tile 2. (**f**) Ground Truth map for Tile 2.  
* Reduction of inference time
* Reduction of memory footprint: the model weights are encoded as 32-bit floating point variables. To further speed up the inference procedure and limit the overall model size, weights are usually converted to 16-bit floating point. This conversion can sometimes affect the model performance, but, in most cases, the impact is negligible. Under these assumptions, we can estimate the final model size: \[\text{HAFNet:}88.978\left(Millionsofparameters\right)\approx 360\,\text{MB}\] vs.  
The used memory can be further compressed to a 8-bit fixed point systolic array in order to make the model directly deployable to dedicated AI platforms, such as Intel's Myriad 2 or Google's Google Coral 28-nm Tensor Processing Unit (TPU) that features 8 MB of on board memory. The memory footprint of the proposed model is much smaller than that of the reference one. Moreover, its computational and power demand are small; all these factors make it suitable for on-board processing in spaceborne Earth observation platforms.  
As we could assess from the recorded metrics, the HAFNetE model can reach state-of-the-art classification performance. However, the most noticeable and relevant advancement from the previously proposed HAFNet model is the efficiency of the overall network. As described in Reference [9], EO Deep Learning applications are currently relegated to offline processing because models are not properly designed for operating at the edge. In most of the cases, model topology and effective number of parameters are too large to comply with satellites memory and power consumption requirements and that strongly limits the impact that Deep Learning can give to Earth Observation systems. HAFNetE has been engineered taking into account all these requirements and with a deployment-oriented approach. Classical models often disregard memory and computing limitations and, therefore, generally end up not being suitable for deployment as on-board spaceborne systems. HAFNetE represents an example of what DL can provide as an effective tool in real-world EO applications that can work directly on satellites and, consequently, empower new industrial possibilities.