AI for Urban Analytics, Prediction, and Design

The project iuses Artificial Intelligence for improving architectural practices to foster the development of sustainable urban environments.

AI for Urban Analytics, Prediction, and Design
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About

This project leverages artificial intelligence (AI) to enhance urban planning and architectural design and foster the development of sustainable urban environments. By integrating AI technologies, such as machine learning (ML) and evolutionary algorithms, the project seeks to revolutionize how urban spaces are examined, predicted, and designed, ensuring they meet the demands of future society while promoting sustainability.

Predictive Analysis

We utilize historical urban and socio-demographic data to forecast the impacts of urban renewal projects on society. This predictive capability will allow stakeholders to make informed, data-driven decisions, minimizing risks and maximizing the benefits of urban development initiatives. By understanding social impact, urban planners can better strategize and implement projects that align with long-term sustainability goals.

Optimization

Optimization research is a critical component of our project. We employ advanced algorithms to analyze vast amounts of data and search for the best solutions.

Funding

  • Ministery of Science and Technology
  • Ariel University
  • Green Group LTD

Related Papers

2024
Haya Brama, Agata Dalach, Tal Grinshpoun, Jonathan Dortheimer (2024). Towards a Robust Evaluation Framework for Generative Urban Design. eCAADe proceedings 10.52842/conf.ecaade.2024.1.529
Haya Brama, Tal Grinshpoun, Agata Dalach, Jonathan Dortheimer (2024). Challenges in the Evaluation of Machine Learning Techniques in Generative Design. Elsevier BV doi:10.2139/ssrn.5036299
2022
Anna Boim, Jonathan Dortheimer, Aaron Sprecher (2022). A Machine-Learning Approach to Urban Design Interventions In Non-Planned Settlements. CAADRIA proceedings doi:10.52842/conf.caadria.2022.1.223