Specialist Diploma in Smart Urban Technologies


Key information

Edition: First
Hours: 825.00
Language: English
Start date: 18/10/2017
End date: 07/09/2018
Credits: 33.00 ECTS
Code: 17PQF3006
Open enrollment

Price: 4.390,00€
Timetable: Wednesday and Thursday from 18:00 to 21:00, Friday from 17:00 to 21:00 and Saturday from 9:00 to 14:00
Venue: Polytechnic School of the University of Girona and Science and Technology Park of the University of Girona.


This diploma has been designed as a module contained within the master’s degree in Smart Cities. The course runs concurrently with the master’s degree, sharing the same schedule and calendar but with fewer credits.


To train professionals to lead cross-disciplinary teams for Smart City projects anywhere in the world.

To offer university graduates specific training in information technologies, city planning and socioeconomics in order to deal with present-day challenges in modern cities.

Specialist Diploma in Smart Urban Technologies it's part of the Master's degree in Smart Cities. During the master’s degree the students complete an on-site practicum in different cities around the world where they develop real smart city projects working alongside city public authorities. Students who complete the programme will be qualified to work in the new and promising profession of smart urban technologist, an advantageous position from which to enter the incipient market for smart city solutions and consulting, as well as to deepen their knowledge in research careers.


Anyone interested in Smart Cities. We understand that the Smart City concept is broad, ranging from more tech-oriented profiles to more social or economic ones.


There is no specific requirement for candidates.


Briefings > june 22 and september 21 at 18 pm in the classroom 4-5, Building Giroemprèn (Science and Technology Park University of Girona).


Block 1: October to January (face-to-face courses)

1. Urban planning and ICTs: concepts and initiatives

Review of various urban initiatives leading up to the Smart City: new urbanism (“smart code”), ruralism, fractal city, sustainable city, liveable city, knowledge city, creative city, digital city, smart city. Smart City experiences in the new city and existing city. Planned city case study. Transformed city case study. A look at different methodologies for urban planning (systematisation, standardisation, etc.). Differences between urbanism and urban planning. Study of the following concepts: planning 2.0, e-planning, web-based planning, etc.

2. Measuring urban smartness and sustainability 

Smart cities build on the sustainable city: existing indicators for measuring urban sustainability and necessary indicators for measuring urban smartness. New representation metrics for measuring urban smartness: Neogeography, Applied Geography, Geostatistics and spatial simulation, Spatial statistical models, Space temporal modelling, Collaborative mapping, Geotagging, Volunteered geographic information, Ontologies for urban planning, City Gml, Maps mash up, Tangible maps and planning. Control of urban systems: traffic, pollution, smart watering of public places (timers and programmers complemented with weather forecasting technology, humidity data, etc. to adjust irrigation, etc.). Smart environment: monitoring of air quality, water quality, noise, humidity, temperature, nocturnal light pollution.

3. Urban visualisation techniques 

Virtual reality and modelling techniques. Description of techniques: remote sensing, 3D models and urban modelling in general, dynamic modelling, etc. Geovisual analytics, geovisualisation, data analysis for visual exploration. Visualisation and modelling of tracking data. Geographical Information Systems. Representation of geolocalised data and user maps: representation of data mining, representation of ubiquitous mobility, of mobile software activity, mobility maps (traffic in real time, etc.), mapping of anonymous data (urban flows, time patterns, etc.), combining user maps with open data, deformed maps (maps in continuous deformation according to a specific criteria).

4. Data analysis and data mining 

Data mining and automated learning techniques: Generic module on artificial intelligence. Automated learning and knowledge discovery techniques. Fuzzy and rough sets, logic and reasoning and spatial extensions. Ontologies for spatial analysis. Spatial data mining and analysis: focus on spatial and sequential data mining, management of spatial data (spatial data warehouse and spatial OLAP - Online Analytical Process). Decision support systems (DSS) in (spatially) distributed environments.

5. Communication and information infrastructures 

Telecommunication networks: Technologies for telecommunication infrastructures: Wireless networks (Wireless LAN -WLAN-, Wi-Fi and HiperLAN -IEEE 802.11 -, Wireless Metropolitan Area Networks (WMAN), MDS, WiMAX, and HiperMAN, etc.), wireless access to public networks, virtual and corporative networks, TCP/IP architecture, internet services, multiservice networks, routing and quality of service resource management, privacy and security.  Sensor networks:  ZigBee, EnOcean; Personal area networks, Bluetooth, TransferJet, Ultra-wideband (UWB from WiMedia Alliance), web-sensors, etc.

6. Expert seminars 

Seminars led by external experts on specific themes

Block 2: February to September (online projects)

City project

The city project is a supervised practicum integrated in the structure of the master’s degree. It’s completed in the second half of the programme. The city project consists of a training activity in which students apply the knowledge acquired in the first five unit blocks in order to find solutions to the real needs of real cities. The practical part of the city project lasts three months. Supervised by their degree tutors and tutors of their chosen cities, students work both on-site and online with their projects (two visits to the city during the three-month period: one at the kick-off and one halfway through the period). To complete the project, students must submit a project report and make an oral presentation of the results obtained.

Modular structure

This course is part of a program that includes the ability to enroll independently following qualifications:

Master in Smart Cities

Specialist Diploma in Smart Urban Technologies

Diploma in Innovation for Smart City Projects

Teaching staff

Pending confirmation.


Dr Josep Lluís de la Rosa. Has been a full professor at the University of Girona (UdG) Spain since 2010 and previously at the Rensselaer Polytechnic Institute (RPI), New York, USA (2008-2010). Director of the EASY  Research Centre and of the Master’s Degree in Smart Cities of the UdG. He holds an MBA from the UdG and a PhD in Computer Science from the Autonomous University of Barcelona (UAB). De la Rosa is an expert in intelligent agents, social networks, virtual currencies and digital preservation and their application to the market. He has contributed more than 200 international publications and has supervised more than 20 PhD theses. He’s a researcher with entrepreneurial vision who has created several spin-off companies, starting with the world’s first robotic soccer team as far back as 1996. His research into complementary and virtual currencies started in 2006 and he soon became fascinated by the disruption of the Blockchain and SmartContracts technologies and their advantages. Since then, he has been working on this theme in order to design new types of money suitable for the Internet in particular, as well as in many other applications.


Dr Andres El-Fakdi. Is an assistant lecturer and researcher in the Department of Electrical Engineering, Electronics and Automation of the University of Girona. He’s the Laboratory director and promoter of the EASY Centre research group. He’s also a member of the coordination team of the Master’s Degree in Smart Cities of the University of Girona. He holds a Degree in Electrical Engineering from the University of Girona and a PhD in computer science from the same university. His research interests are focused on contributing to the development of machine learning techniques for Decision Support Systems (DSS) in order to increase productivity and effectiveness in complex scenarios. His PhD research focused on the study and development of machine learning techniques and its application to robotics. This research successfully concluded with the use of learning algorithms to overcome non- programmed changes in the environmental conditions that lead an autonomous robot to fulfil a particular task. Meanwhile, his post-doctoral research focuses on the study of Big/Open Data applications and on the design and development of similar machine learning solutions for knowledge discovery.

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