SMART CITIES

Geographical Information System + Big Data + Machine Learning

Senseta uses a practical and integral approach for city management – technologies oriented to improving the citizen’s experience within the urban scope – in which the concept “Senseta City Management (SCM)” comprehends the assembly (space and time layers), operation (geoprocesses) and maintenance (evolution) of Big Data Smart Geographical Information System (GIS) on which Machine Learning techniques are continuously applied to generate “tailored” models. This is applied under the premise that all cities in the world are different so a model that worked for one, might not work for another.

Senseta's tools allow our governmental clients to build comprehensive models and monitoring of the city’s daily operation models using Machine Learning and Big Data Techniques. Applications for this technology include predictive analytics of:

  • Drinking water supply
  • Energy supply
  • Sewage network behavior
  • Telecommunications network simulations
  • Food supply
  • Waste handling
  • Transportation
  • Health and education services
  • Urban planning
  • Safety and governing policies
  • Risk analysis of potential catastrophes and natural disasters
  • Emergency response and contingency plans

Senseta's tools provide citizens and their leaders with a framework to realize planning, formulate policies and make better decisions based only and exclusively on data on which specific models for each city are built. As a consequence, a smart city starts to achieve balance with the environment.





Find out more about Senseta's Smart Cities approach solution by downloading our 2 pager document here.

  • Colombian Navy

    Big Data helps empower the Colombian Security Miracle.

    Using a Senseta BDAC, the Colombian Navy has successfully implemented it's Data Fusion Intellence Center. This Analytical facility fully assembled by Senseta, has already improved significantly the response time to threats and the quality of the Navy's intelligence products.

    Senseta's solution has solved the Navy's most difficult technological problems, and as a direct result Colombia's intelligence officers can now concentrate on securing the country against those who seek to do harm.

  • What's going on with traffic?

    Understanding traffic patterns of one of the most populated cities in the World.

    Using it's proprietary Spatial Machine Learning Technology, Senseta provided a state of the art predictive system for traffic analytics for the city of Mumbai in India.

    The current system, powered by Senseta technology is capable of processing millions of GPS logs to obtain the best possible prediction of future traffic conditions.

  • Ooredoo

    Ooredoo's partnership with Senseta will enable companies in Qatar to analyse huge volumes of data to uncover hidden patterns, correlations, and other key business insights.

    The Senseta/Ooredoo pilot programme used the BDAC solution to analyse multiple data sets to predict and optimise Ooredoo’s mobile network utilisation. The results generated showed the significant business potential of using big data analytical methods and modelling for business planning and network optimisation.

  • Cracking an 18 year-old Cold Case

    Senseta’s MAX 5.0A Rover, an autonomous ground vehicle, used research algorithms and Senseta technologies to collect data and locate buried material evidence, which led to a conviction and resolution of the 18 year old cold-case.

    Based on this data, the county (San Jose, California) excavated the site and retrieved car parts that matched the suspect’s car. Confronted with this evidence in court, on August 29, 2009, the suspect pleaded no contest to the charges, was convicted of manslaughter, and sentenced to six years in prison.

  • Location, location, location

    Empowering one of the largest bank in Latin America

    Using Senseta's proprietary technology, one of Latin America's largest banks applied a combination of geospatial intelligence and machine learning techniques, to visualize in a dynamic map all the relationships between transactions, people, organizations and places, over time.

    This analytical insight allowed it to make a better decision on how to locate it's branches and operations.