## 4 Discussion and Future Work  
Our system was able to successfully design full networks that, trained from scratch (without any manual fine-tuning or data augmentation or application of ensembles), yielded for the best scoring model a test accuracy of 90.6-93.1% with kfold (k = 5) and 91-93.8% (with k = 10). Therefore, the GGGP NAS approach shows results similar to well-known architectures. The main disadvantage we find is the time it takes to run the evolutive algorithm. We should note that, as mentioned above, some of the meta-grammar parameters were set in our tests according to hardware constraints (e.g., GPU memory) and therefore should not be interpreted as optimal settings in any case. Exploring ways of efficiently incorporating transfer-learning and data augmentation, fine tuning the system and adapting the grammar for pixel-level semantic labeling architectures are some of the next steps of the Cartobot project. On the other hand, meta-grammars allow new applications of the system as a hypothesis checker using different variations of meta-grammars and comparing the results obtained. For example: Is it better to apply dropout only at a certain stage of the architecture? What is the optimal value for the dropout rate? We are currently working on methods to speed up the design process, from reducing the number of epochs at early design stages to using caching methods for the convolutional block weights or migrating to training on cloud servers. Research is also being conducted on ways to gain reusable knowledge among different evolution experiments or problem domains. Finally, to improve our \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accuracy** & **Accuracy** & **Accuracy Mean, with** & **Accuracy Stdev, with** \\  & **Mean** & **Stdev** & **Data Augmentation** & **Data Augmentation** \\ \hline c10g49m2 & 0.911 & 0.003 & 0.918 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test metrics for best VGNet’s designed model for the CIFAR10 dataset. Figure 7: Example of a network automatically designed by VGNet. High-level architectural overview of model “v2m2”. Numbers indicate from top to bottom: (**i**) “S” and “C” blocks: size, number of filters and dropout rate; (**ii**) “A” and “M” blocks: size; (**iii**) “D” dropout rate; and (**iv**) “F” number of units. method's capability of balancing exploration/exploitation during the NAS, future research will further explore enhancements to the presented SDF mutation operator. Conceptualization, V.d.I.F.C, A.D.-A. and F.S.G. ; methodology, V.d.I.F.C., A.D.-A. and F.S.G. ; software, V.d.I.F.C. ; validation, V.d.I.F.C., A.D.-A. and F.S.G. ; formal analysis, V.d.I.F.C. ; investigation, V.d.I.F.C. ; resources, V.d.I.F.C., A.D.-A., M.-A.M.-C. and F.S.G. ; data curation, V.d.I.F.C. ; writing--original draft preparation, V.d.I.F.C. ; writing--review and editing, V.d.I.F.C., A.D.-A., M.-A.M.-C. and F.S.G. ; visualization, V.d.I.F.C; supervision, F.S.G. ; project administration, M.-A.M.-C.; and funding acquisition, F.S.G. and M.-A.M.-C. All authors have read and agreed to the published version of the manuscript. This research was partially funded by the Instituto Geografico Nacional (IGN, Spain). The authors acknowledge the staff at the Instituto Geografico Nacional (IGN, Spain) and all team members of the Cartobot project; [PERSON] for her support and additional graphic design; and [PERSON] for additional draft reviewing and suggestions. The founders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. * [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] [PERSON] Automatic vegetation identification and building detection from a single nadir aerial image. _Remote Sens._**2009**, \(1\), 731-757. [CrossRef]
* [PERSON] et al.