Description
Extensive research has been carried out to advance computational tools that support the development of smart cities. In particular, the use of data mining and machine learning techniques has become central to designing more efficient, safer, and more pleasant urban environments. For example, imagery acquired from camera networks in public spaces can be analyzed to estimate city-square occupancy in real time and to predict future usage patterns using machine learning models. Such approaches can support improved decision- making for public safety, resource allocation, and urban planning.
The main goal of the 4th Workshop on Data Mining and Machine Learning in Smart Cities (WDMMLSC 2026) is to bring together researchers working in areas such as Data Analytics, Pattern Recognition, Prediction and Simulation, Artificial Intelligence, Signal Processing, Computational Vision, Informatics, and Software Development, among others. The workshop aims to discuss current trends, identify open challenges, and outline future research directions in the context of smart and sustainable cities.
WDMMLSC 2026 welcomes contributions from academia, industry, and public entities involving fields related to Data Mining, Artificial Intelligence, Machine Learning, Signal and Image Processing, Management, Architecture, Smart Cities, City Authorities, and Policy Makers. The workshop strives to foster interdisciplinary collaboration and to encourage the development of innovative solutions for more efficient, sustainable, and human-centred cities.
Topics (not limited to)
- data preprocessing, classification, and visualization
- big data and data analytics
- dashboard design
- service design
- human–computer interaction
- feature extraction, selection, and classification
- machine learning and deep learning
- pattern recognition and clustering
- signal processing and analysis
- computer vision and image processing
- traffic analysis, prediction, and simulation
- route optimization and planning
- user and usage characterization
- surveillance systems
- computational decision-making
- applications of data mining tools in smart cities
- applications of machine learning tools in smart cities
- software development for smart cities
Organizers
João Manuel R. S. Tavares (Main Organizer)
Faculdade de Engenharia da Universidade do Porto, Portugal Email:
URL: https://www.fe.up.pt/~tavares
ORCID: https://orcid.org/0000-0001-7603-6526
Marta Campos Ferreira (Co-Organizer)
Faculdade de Engenharia da Universidade do Porto, Portugal Email:
ORCID: https://orcid.org/0000-0001-9505-5730
Program Committee
Alexandre Xavier Falcão — Universidade de Campinas, Brazil António Lobo — Universidade do Porto, Portugal
Arrate Muñoz Barrutia — University of Navarra, Spain
Constantine Kotropoulos — Aristotle University of Thessaloniki, Greece Fiorella Sgallari — University of Bologna, Italy
Francisco Perales — Balearic Islands University, Spain Hemerson Pistori — Dom Bosco Catholic University, Brazil
Javier Melenchón Maldonado — Open University of Catalonia, Spain Joana Hora — Universidade do Porto, Portugal
João Paulo Papa — Universidade de São Paulo, Brazil Jorge M. G. Barbosa — Universidade do Porto, Portugal Jun Zhao — Shanghai Jiao Tong University, China
Manuel González Hidalgo — Balearic Islands University, Spain
Mario F. Montenegro Campos — Universidade Federal de Minas Gerais, Brazil Michael Liebling — University of California at Santa Barbara, USA
Reneta P. Barneva — State University of New York, USA Roberto Bellotti — University of Bari, Italy
Sérgio Duarte — Universidade do Porto, Portugal Teresa Galvão — Universidade do Porto, Portugal Valentin Brimkov — State University of New York, USA
Victor Hugo C. de Albuquerque — Universidade de Fortaleza, Brazil
