## 1 Introduction
Some examples of such approaches include the use of reinforcement learning techniques, as seen in [46; 47; 48], or the application of evolutive algorithms (e.g., [49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60]). Some systems use grammars to define the rules to obtain valid structures that can encode either the neuron connectivity matrix and some other form of network topology [54; 56; 57] or other higher level expression of the operations being performed by the network layers and their connectivity [52; 53; 55; 58; 59; 60; 61]. Some of those systems (e.g., [52; 53]) work by generating architecture expressions with high-level operations (such as convolutions, activation functions, dropout, etc.). In [53], the architecture is sequential in term of layers, while [PERSON] et al. [52] allowed more complex connectivities (parallel branches inside the network). The present work is conducted inside the Cartobot project for the Instituto Geografico Nacional (IGN, Spain) to perform automatic design of deep neural networks for road detection on aerial images, guiding the search with information about useful network structural patterns. This project is of real application on a dataset constructed (by IGN and Cartobot personnel) from Spanish National Plan for Aerial Orthophotometry (PNOA) aerial orthoimages. The two main research goals are: (a) design a grammar that can successfully encode the most typical high-level architectural blocks of deep ANNs; and (b) build a NAS system capable of designing deep ANNs using that grammar for the purpose of successfully detecting roads on aerial images. We divert from the approach of [PERSON] et al. [52] in that our grammar is going to define specific high-level blocks, which is discussed below, instead of allowing fully free connectivity for the generation of the blocks with parallel branches. We also work on a dynamic mutation operator linked to the structural diversity of solutions (population) being contemplated at each instant. We start by reviewing some of the well-known network models (AlexNet [62], VGG [63], GoogleLeNet [64], RestNet [65], Inception-v3 [45], Inception-v4 and Inception-ResNet [66], DPN [67] and ResNeXt [68]) used by top scoring results on the Imagenet Large Scale Visualization Recognition Challenge (ILSVRC) [69] (editions from 2012 to 2017) where many different architectural patterns can be found. As mentioned, by defining a meta-grammar able to condense some of such structural rules,  
Figure 1: Examples of potential issues: (**a**–**f**) roads with complex contextual terrain or partial occlusions; and (**g**–**o**) images not containing roads that could be hard to classify. we aim to guide an evolutive NAS system to find networks capable of solving the road detection problem on aerial orthoimages. The problem of road detection (including secondary roads) on such orthoimages has, as explained above, the added difficulty of natural or artificial structures that can be mistaken with roads or transportation infrastructures. Some samples of those formations can be seen in Figure 1g-o with various levels of complexity. On the other hand, many different types of roads are being considered and are required to be detected by the system. We have a set of sample orthoimages with their corresponding numerical topographical cartography at a scale of 1:25,000 (BTN25) to allow for easier identification of the roads by the reader (see Figure 2).