SEN2ROAD: Roads are also visible using Sentinel

Our model employs the most advanced AI technology to make the segmentation of city roads possible

Today there is an abundance of digitized cartographic information on different artificially created elements, especially roads and highways.

In the most developed countries roads of cities of considerable size are very well mapped through companies’ global services, or initiatives such as OSM (Open Street Map). The quality and even existence of this road mapping and its layers, however, declines as the size of the city decreases, and when they are located in developing countries with limited resources.

To all this it must be added that these layers of information are updated at very different rates, depending on their geolocation. On continents like Africa, for example, the cartographic information is very poor, or outdated, which generates a lack of knowledge and control in areas such as mobility and public services.


At Tracasa we have tackled the challenge of extracting highways and roads from images drawn from the constellations of Sentinel satellites. As is known, the spatial resolution of Sentinel-2A images is some 10 meters, in many cases is insufficient for proper segmentation of the terrain. This is why we had to rely on our SENX4 super-resolution model to enhance images and generate more precise segmentation.

Thanks to the advances in our SEN2ROAD model for the segmentation of roads and highways, a wide range of possible uses opens up:

-Rapid Mapping. Access to quick road maps

-Logistical and mobility planning

-Disaster responses of all kinds

-Analysis of urban development and carbon footprints

-The creation and updating of cartography


Satellite images are an invaluable resource for all remote sensing and territorial segmentation activities. Today, the availability of road segmentation models functioning at an adequate level is highly dependent on the spatial resolution of the satellite images used. Generally, in these cases images featuring a resolution of 2.5 meters or less are used, and they entail a high cost.

Tracasa, with the help of its super-resolution SENX4 model, is making it possible to use free- access images such as those from the Sentinel missions for this type of work, thereby greatly reducing the cost and making it possible for more companies, institutions and individuals to access these services.

SEN2ROAD is our model for the segmentation of roads and highways using Sentinel images, generally urban, and of highways, at any location worldwide. It is a model that employs the most advanced AI technology to get results, given the difficulty of this type of segmentation through Sentinel images.

Road segmentation in the city of Barcelona, with the Avenida Diagonal in the center of the image.
An aerial view of Barcelona, including its entire municipal area, featuring segmentation of its roads.
A segmentation of roads and highways in the city of Huesca.


To correctly interpret the results of our model, we employed the most widely used metrics for the assessment of segmentation models in the field of Deep Learning. As can be seen on the following table, SEN2ROAD has already achieved metrics close to 0.86 in PPV and 0.72 in mIoU – until now unthinkable for road segmentation models using Sentinel images.

Image of Bilbao featuring road segmentation.

An aerial image of Seville, with a segmentation of its roads and highways.


The segmentation of highways and roads at the pixel level on site is of great value in terms of spatial representativeness, but it is insufficient if it is to be used for decision-making in the areas of planning, communications and mobility.

With a view to making the most of the segmented information through our SEN2ROAD model, we have continued to advance with our optimized vectorization of rasters.

Through our SEG2VECTOR algorithm library we are able to vectorize the raster information of roads and highways in an extremely optimized way. Thus, we have obtained a Shape format with all the structured information on the roads. This type of vector information makes possible the simulation of transport networks and subsequent studies based on mobility criteria.


Urban development study

The world’s population continues to grow, inexorably, and its concentration in large urban centers has negative environmental effects. This growth can have a very negative impact if it occurs without observing rigorous energy efficiency standards.

The generation of a hypertemporal urban development index allows us to establish, based on remote sensing techniques, the growth patterns of cities and study their energy and environmental impacts. In this way a valuable tool for studying citizens’ mobility is made available to governments.

Tesla Gigafactory in Berlin-Brandenburg (Germany).

Mobility and the calculation of alternative routes

In many cases involving reduced mobility due to the impact of natural environmental disasters, the quick mapping of roads is extremely important in order to be able to indicate alternative routes.

Route calculation within the network of nodes and vectorized segments.

Keeping maps as up to date as possible provides a very valuable layer of information when it comes to meeting mobility needs in areas that may not have the infrastructure or sufficient means to deal with the effects produced by a catastrophe.