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 :
A multi-armed robot for assisting with agricultural tasks
Robohub and Lucy Smith
27 Mar 2026
How can a robot safely manipulate branches to reveal hidden flowers while remaining aware of interaction forces and minimizing damage?
Resource-constrained image generation and visual understanding: an interview with Aniket Roy
Lucy Smith
26 Mar 2026
Aniket tells us about his research exploring how modern generative models can be adapted to operate efficiently while maintaining strong performance.
RWDS Big Questions: how do we highlight the role of statistics in AI?
Real World Data Science
25 Mar 2026
Next in our series, the panel explores the statistical underpinning of AI.
A history of RoboCup with Manuela Veloso
Lucy Smith
24 Mar 2026
Find out how RoboCup got started and how the competition has evolved, from one of the co-founders.
Information-driven design of imaging systems
BAIR blog
23 Mar 2026
Framework that enables direct evaluation and optimization of imaging systems based on their information content.
Machine learning framework to predict global imperilment status of freshwater fish
Oregon State University
20 Mar 2026
“With our model, decision makers can deploy resources in advance before a species becomes imperiled.”
Interview with AAAI Fellow Yan Liu: machine learning for time series
Lucy Smith
19 Mar 2026
Hear from 2026 AAAI Fellow Yan Liu about her research into time series, the associated applications, and the promise of physics-informed models.
A principled approach for data bias mitigation
Bruno Scarone
18 Mar 2026
Find out more about work presented at AIES 2025 which proposes a new way to measure data bias, along with a mitigation algorithm with mathematical guarantees.
