## 1 Introduction  
Two of the main problems with remote sensing information as noted by [PERSON] et al. [1] are that the high volume of data exceeds by far the capabilities of human analysis (by manual revision) and at the same time is usually crucial to perform classification of said data. Under this category of data, large-scale aerial image processed as orthophotography is a useful source of information for many domains. To give some examples of the broad array of applications we can find, there have been systems developed for the detection of coastline changes [2], snow avalanches [3], fires [4],bodies in disaster sites [5], trees [6], seedlings [7], roofs [8], transmission towers [9; 10], vehicles [11; 12], photovoltaic arrays [13; 14], vegetation and buildings [15]. We focus on road detection on aerial images being it an important subject, among other things, due to the need to constantly update road maps. This importance is clear by the volume of research conducted and applied systems developed on this domain. As an object of detection, [PERSON] et al. [16] divided the road characteristics into four main groups: spectral properties (those regarding surface characteristics), geometric (such as width and curvature), topological properties (those derived from their role as part of a transportation network), and contextual (e.g., different types of roads may have different restrictions to their shape, size or materials). Put in the context of aerial image we can find many complications related to those groups or properties some even caused by the geographic context of the roads. One of these problems as shown in Figure 1a,b is due to surrounding terrain having spectral properties similar to the secondary roads shown. Other common problem are occlusions due to their contextual properties (e.g., secondary mountain roads). Here, we show an example of said occlusion at forest areas (Figure 1c-f), on the first case due to the shadows and the rest the trees themselves covering the road. Other problems such as some natural or artificial structures being similar to roads are shown in Figure 1g-o. Some road detection systems rely entirely on non-neural models: [PERSON] et al. [17] combined texture information extraction with a knowledge-based road extraction and a post-processing step for noise elimination; [PERSON] et al. [18] applied Structural Support-Vector Machines (S-SVMs); and [PERSON] et al. [19] and [PERSON] et al. [20] used GraphCut [21]. Although we can find many other systems that rely on road characteristics analysis [16; 17; 18; 20; 22; 23; 24; 25; 26; 27; 28], most recent research has favored the application of deep artificial neural networks (ANNs). We find some systems include ANNs as part of their detection pipeline (e.g., [29; 30; 31; 32; 33]) or just preprocess the input images before they are feed to the network as in [34]. It is frequent to rely on a deep ANN to perform the whole road detection/segmentation process by either developing a novel network or using a preexisting architecture. Some researchers use custom network architectures [35; 36], others customize well-known models [30; 37; 38; 39; 40], use those well-known models with transfer learning [41; 42] or combine them into road-detection ensembles [43]. Systems that apply deep learning techniques to detection on such images can use quite different network dimensions and architectures. We can find ones that go from relatively small convolutional networks, such as the one in [44], to others using models such as the Inception-V3 architecture [45] combined with transfer learning, as in [14] for solar panel detection. To try and solve the need to find a quasi-optimal ANN architecture automatically, Network Architecture Search (NAS) has been extensively researched by using many different approaches but has proven to be a complex task given the dimensions of the search space.