ΑΙhub.org
Smart cities and AI
More than 68% of the world’s population live in highly densely built cities. Those cities are not only causing high emissions and urban heat island impacts on the environment, they are also the most vulnerable areas for the impacts of climate change. Thus, immediate climate adaptation of cities is necessary.
AI-based methods not only allow us to automatize observations and make future predictions about the climate change related indicators of cities, they also help us to understand those indicators better by using explainable AI techniques. In this lecture, you will see a brief introduction to our studies in this field.
https://youtu.be/-iRTUMl9T_c
tags: Focus on sustainable cities and communities, Focus on UN SDGs
Beril Sirmacek
is an associate professor at Saxion University of Applied Sciences
Beril Sirmacek
is an associate professor at Saxion University of Applied Sciences
AIhub is supported by:
Related posts :
Interview with Xinwei Song: strategic interactions in networked multi-agent systems
Lucy Smith
16 Apr 2026
Xinwei Song tells us about her research using algorithmic game theory and multi-agent reinforcement learning.
2026 AI Index Report released
AIhub
15 Apr 2026
Find out what the ninth edition of the report, which was published on 13 April, says about trends in AI.
Formal verification for safety evaluation of autonomous vehicles: an interview with Abdelrahman Sayed Sayed
Lucy Smith
14 Apr 2026
Find out more about work at the intersection of continuous AI models, formal methods, and autonomous systems.
Water flow in prairie watersheds is increasingly unpredictable — but AI could help
The Conversation
13 Apr 2026
In recent years, the Prairies have seen bigger swings in climate conditions — very wet years followed by very dry ones.
Identifying interactions at scale for LLMs
BAIR blog
10 Apr 2026
Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencies and patterns.
Interview with Sukanya Mandal: Synthesizing multi-modal knowledge graphs for smart city intelligence
AIhub
09 Apr 2026
A modular four-stage framework that draws on LLMs to automate synthetic multi-modal knowledge graphs.
Emergence of fragility in LLM-based social networks: an interview with Francesco Bertolotti
Ella Scallan
08 Apr 2026
Francesco tells us how LLMs behave in the social network Moltbook, and what this reveals about network dynamics.
Scaling up multi-agent systems: an interview with Minghong Geng
Lucy Smith
07 Apr 2026
We sat down with Minghong in the latest of our interviews with the 2026 AAAI/SIGAI Doctoral Consortium participants.
