Future Cities
The Future Cities research domain at GATE Institute explores how Big Data, Artificial Intelligence, and the Internet of Things can shape more intelligent, sustainable, and people-centered cities.
Introduction
GATE researchers develop advanced city digital twins — dynamic, semantically enriched 3D models that connect data from various urban systems.
These digital twins allow city planners and policymakers to:
- Simulate, analyze, and predict complex urban processes
- Support evidence-based decision-making in areas such as:
- Mobility
- Infrastructure
- Energy efficiency
- Environmental health
- Climate resilience
GATE’s Future Cities research contributes to creating healthier, more livable, and inclusive urban spaces through:
- Interdisciplinary work in GeoAI
- Immersive visualization
- Semantic interoperability
Focus Areas
CFD Simulations
We use computational fluid dynamics to model airflow, heat transfer, and pollutant dispersion in urban environments, helping to design climate-adapted and healthy cities.
Urban Air Quality
We build spatial models that forecast air pollution by integrating sensor, traffic, and meteorological data, supporting environmental planning and policy
Urban Heat Island
We study how urbanization influences temperature and develop data-driven mitigation strategies such as green roofs and reflective materials
Vegetation ADE for CityGML
We extend the CityGML 3.0 standard to better represent vegetation in cities, enabling digital twins to simulate cooling effects and carbon absorption.
City Digital Twin
We create 3D digital twins that integrate multi-source urban data to support simulation, scenario analysis, and data-driven decision-making.
City Data Visualization
We design interactive 3D and immersive (VR/AR) visualizations that make complex urban data intuitive and engaging.
City Walkability
We develop models that assess walkability, focusing on accessibility, safety, and user experience to promote healthier urban mobility.
Targeted R&D Directions
High-resolution urban microclimate simulation using detailed CFD models for wind, heat, and pollutant dynamics
AI-enhanced air quality forecasting combining machine learning with sensor networks for improved accuracy
Urban Heat Island mitigation through simulation and monitoring of data-driven solutions like green roofs and vegetation
Standardization of urban vegetation data through CityGML 3.0 extensions for interoperability in digital twins
Multimodal urban digital twins integrating geospatial, sensor, behavioral, and simulation data for scenario planning
Immersive urban data exploration using AR/VR for intuitive understanding of city-scale datasets.
Walkability modeling and policy support tools that enhance livability and accessibility.
GeoAI applications for predictive urban planning and risk assessment.
Team & Collaborators
Lidia Vitanova, PhD, Eng
Simeon Malinov
Emil Hristov
Desislava Petrova-Antonova, PhD
Scientific Publications
Let’s collaborate
Whether you’re from academia, industry, or public sector, GATE offers unique partnership opportunities in research, infrastructure, and innovation