## 6 Visualization, Limitations and Future Directions  
Our efforts were focused next on building a web platform that combines the model's predictions with existing cartographic support. This platform allows an operator to select a geographic extent and feed that area to the prediction system, obtaining a segmentation result and allowing the user to view it overlaid onto the orthoimages, with different presentation styles. The architecture of the web platform is presented in Figure 8 and represents a novel approach to semantic segmentation of very large areas. The _display subsystem_ consists of a _web viewer_ where the work area is displayed. The information represented is a layer of aerial ortho-photography from a WMS-Tiled service, offered by the PNOA. This layer is integrated into the map through the Openlayers libraries, which also allow controls for the selection and deletion of areas on the map. Once the workspaces are selected, the _tile registry_ component generates a list of URLs addressing tiles of zoom level 18 (scale 1:2257), and supplies this information to the prediction engine, along with the operator's preferences for displaying results. The _model selection_ component maintains a record of the prediction models delivered and available for use on the web platform in h5 format. The operator can select which model of those available in the _model repository_ he wants to use to make the prediction. Figure 8: Architecture of the web platform. The _prediction subsystem_ receives the information of the selected model, the display preferences, and the list of tile URLs to process. With this information, it performs tile downloads for the work area, if they have not been previously downloaded, and, in the _prediction engine_, applies the semantic segmentation function to each tile, according to the selected model. The result of this process is a set of segmentation matrices to which the tile generation module applies a colour ramp and a transparent background if the operator has selected it (an example can be seen in Figure 9). These generated tiles are stored in a temporary directory, inside the _tile repository_, which is returned to the _display subsystem_, which adds this set of tiles as a new layer in the _web viewer_. The layer selection and deselection controls allow the operator to visually compare this result with the application of other models, and with the information on roads present in the orthophoto, in order to adjust post-processing techniques. For post-processing based on Geographic Information Systems (GIS), the platform offers the possibility of downloading a geotiff, georeferenced file with the segmentation results that can be directly imported into the GIS processing program [56]. This file is generated through the _georeferencing module_, using the OSGeo/gdal [57] Python libraries. The main limitations of this study are related to the imperfections present in the reference maps containing the roads. However, backed up by the statistical analysis carried out in Section 5, it was proved that complex segmentation models can obtain high quality results even without having segmentation maps that are accurate at the pixel level and require a considerable tagging effort. Another downside is that even the best segmentation classifier would sometimes omit potential roads that might be identifiable to human experts or would misclassify similar background geospatial objects (dry riverbeds, railroads, or irrigation canals) as roads. We consider that the predictions can be improved by adding more training data from these challenging areas. The intuition is that by exposing a network to more real-life data, we can boost its performance and reduce the FP and FN rates. However, if the problem persists, a multiclass classification could be a preferable approach. Another problem found was related to the overlook of connection points, resulting in unconnected road segments. We have also identified the challenge of inaccurate extraction near the borders due to the lack of context.