Abstract
Digital twins are transforming architecture, urban planning, and infrastructure management by creating intelligent, data-driven replicas of physical assets. These virtual models integrate real-time sensor data, predictive analytics, and AI to optimize performance, streamline decision-making, and enhance sustainability.
In this podcast, we explore the evolution of digital twins with guest Karla Saldano, an expert in urban analytics and AI-driven design. Discussing how digital twins progress from static 3D models to fully autonomous systems capable of real-time decision-making. The conversation covers essential skills for digital twin development, best practices for data integration, and how AI is shaping the future of intelligent urban environments.
Whether you’re an architect, engineer, or tech enthusiast, this podcast provides key insights into how AI-powered digital twins are revolutionizing the built world.
Listen to the podcast here
Guest
Karla Saldaña is an assistant professor at the University of Florida and the dynamic force behind the SHARE Lab, where she pioneers work in AI-driven creativity and urban analytics. With a PhD from ETH Zurich, her research focuses on the powerful synergy between AI and human insight to transform design and enhance disaster response. Carla’s global collaborations span Germany, Italy, Switzerland, Mexico, and Ecuador, making her an undisputed expert in AI and digital twins.
Chapters
0:00 – 1:02: Introduction & Podcast Overview
1:02 – 3:47: Defining Digital Twins & Their Continuum
3:47 – 6:46: Karla Saldano’s Journey: From Architecture to AI
6:46 – 7:30: Intelligent Environments: Beyond Digital Replicas
9:06 – 13:55: Expertise & Collaboration in Digital Twin Development
9:20 – 15:00: Data Integration & AI-Readiness: Static vs. Real-Time Data
17:10 – 19:00: Interoperability & Standards in Digital Twin Pipelines
19:00 – 22:42: Scaling Digital Twins: Data Harmonization & Aggregation
22:42 – 27:05: Data Collection Strategies & Stakeholder Integration
27:05 – 28:15: Innovative Applications & Future Directions
Questions
Icebreaker
1. Defining Digital Twins
🟢 Q!: Digital Twins and AI are two of today’s biggest buzzwords. Let’s start simple—how do you define a digital twin?
🟢 Q2: That’s a great way to frame it! Now about you, What brought you into this field? If you can tell us briefly about your journey?
Questions
1. Defining Digital Twins
🔴 Q3: After defining a digital twin, what advantages do you see in integrating multiple data sources into a single platform?
2. Building a Digital Twin Team
🔴 Q4: Now, when it comes to actually developing a digital twin, what kind of expertise is needed? Can you walk us through the key roles that make a project successful?
3. Data and AI in Digital Twins
🟢Q5: Since data is at the core of any digital twin, I’m curious—how do different types of data, like geometry, sensor data, and operational data, come together to enable AI and analytics?
🟢 Erva: And on that note, what does it mean for a digital twin platform to be AI-ready? What are the key capabilities or features that make AI integration successful?
4. Standards and Interoperability
🔴 Q6: With all these different data types and technologies involved, interoperability must be a big challenge. Are there any current standards or best practices that help digital twins work seamlessly with BIM and AI technologies?
5. AI’s Role in Digital Twins
🟢Q7: AI is changing so many industries, and digital twins are no exception. From your perspective, how is AI transforming the way we design, analyze, and interact with digital twins?
6. Scaling Digital Twin Projects
🔴 Q8: Moving from a single building to an entire portfolio or even a city-scale implementation must be a huge leap. What are some strategies and key metrics for scaling digital twins effectively?
7. Digital Twins at the City Scale
🟢 Q9: Speaking of scale, what does a digital twin look like at the city level?
🟢 Q10: And when working at that scale, which data sources and stakeholders are the most critical to making a city-wide digital twin function properly?
8. Future Applications of Digital Twins
🔴 Q11: Looking ahead, what are some of the most exciting or innovative applications of computational models when a city is represented as a digital twin?
Conclusion
Our conversation with Karla Saldano provided a fascinating look into the evolving world of AI-powered digital twins and their impact on the built environment. As technology advances, it’s clear that successful digital twin projects require more than just powerful tools—they demand interdisciplinary collaboration, thoughtful data integration, and a deep understanding of how spaces function dynamically over time.
One key takeaway is that while AI and predictive analytics are transforming architectural design and urban planning, the true challenge lies in creating workflows that balance technical precision with human insight. The future of digital twins isn’t just about automation or efficiency—it’s about designing smarter, more adaptive environments that serve people in meaningful ways.
As digital twins continue to scale from buildings to entire cities, it’s crucial to prioritize not just data accuracy, but also the diverse expertise needed to shape the built world effectively.
Cover image sourced fromhttps://blogs.idc.com/2021/07/28/the-next-frontier-ai-and-digital-twins-in-smart-cities/
