05/05/2026
Sando Conservación y Energía leads the SOTER project, an innovative R+D+i initiative aimed at the development of an advanced system for the automated inspection of the condition of pavements, based on the use of very high-resolution satellite images and artificial intelligence that allows preventive maintenance to be carried out on large surfaces.
The project, which has the support of the Technological Corporation of Andalusia (CTA), is developed in consortium with the 3D technology company GEOSPACE and with the participation of the RNM368 research group of the University of Almeria, an international benchmark in remote sensing and artificial intelligence applied to the territory.
A research that according to the director of R+D+i, Juan Antonio Báez, will meet the expected expectations: “With CTA’s advice we expect results that represent a strategic advance for the company by strengthening its leadership in innovation. SOTER opens a new path of digital transformation in the road maintenance sector and contributes directly to a safer, more sustainable and efficient society in the management of its infrastructures.”
CTA’s Head of Public Sector, Carlos García, says that “SOTER provides a differential and cutting-edge approach to the maintenance of road infrastructures”. As García explains, the results of this project “will, of course, allow us to address a much faster, more efficient, effective and economical management of this work, but they will also facilitate a deep understanding of the short, medium and long-term response of these, enabling clear improvements in their own design and thus achieving an optimal integration of the entire construction cycle in accordance with the BIM paradigm”.
The main objective of SOTER is to improve road safety and efficiency in infrastructure maintenance through an innovative methodology that allows for the automatic detection, classification and prioritization of existing pathologies on urban and interurban Roads and pavements.
Unlike traditional methods based on face-to-face visual inspections, the solution developed combines very high-resolution satellite images (VHR) with artificial intelligence and machine learning algorithms, which allows large extensions of road to be analysed objectively, accurately and safely.
The system is complemented by 360º georeferenced images captured from a vehicle, which make it possible to validate the results obtained by the models and improve their reliability. The project also incorporates the criteria of the iRAP program, an international standard that evaluates road safety to prioritize improvements and reduce accidents.
SOTER is the result of a continuous trajectory of research and innovation at Sando in the field of road maintenance, infrastructure auscultation and the digitalization of maintenance processes.
In recent years, the company has developed various R+D+i projects focused on improving road management and obtaining objective information for decision-making. Among them, PAVIMENT15, aimed at the creation of intelligent tools for conservation management; LAS ROADS, focused on the extraction of information for civil works from advanced sensors; or AUTOMGIS and INSPECTROADS, aimed at the automatic identification of urban elements and pavement pathologies using geographic information systems and artificial intelligence.
The SOTER project represents a qualitative leap with respect to these initiatives by incorporating a complete territorial vision through satellite images. In this way, the need for on-site roadside inspection is reduced and the technical detection of incidents is connected with international road safety criteria.
In addition to its technological contribution, SOTER will reduce costs, times and risks in road maintenance, improve the planning of investments in road safety, promote qualified employment and promote a lower environmental footprint.
With initiatives such as SOTER, Sando consolidates its commitment to technical excellence and innovation in line with its Sustainability Master Plan, moving towards a safer, smarter and more efficient infrastructure model.
More information in https://sites.google.com/ual.es/soter/inicio
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