Digital Twin of the built environment: From Hyper-local to Global with AI
Evolution: Starting with San Francisco Bay Area neighborhoods, One Concern's digital twins have scaled to envision a global landscape. Powered by AI, our models can recreate and anticipate the impact of climate on the built environments.
Methodology: Our journey began with manual data collection, meticulously sourcing information from local entities. But as we aimed higher, broadening our horizons, our technology stack expanded.
Leveraging predictive AI, foundation models, large-scale impact predictions, and dependency modeling, we've developed models to predict intricate interdependencies within urban systems. By integrating climate projections into our models, we ensure dynamic adaptability, ensuring relevance despite the ever-changing climate challenges.
Our network models delve deeper, tracing dependencies and pipelines, enabling us to comprehend and visualize the intricate web of city utilities, resources, and their interplay.
Transforming Complexity into Actionable Insights
Data-Centric AI: Bridging Urban Data Gaps
Challenge: Cities brim with data, yet piecing this information together cohesively remains a formidable challenge.
Solution: One Concern's multifaceted AI ecosystem harnesses the combined power of predictive analytics and advanced computer vision.
Computer Vision Transformers: At the heart of our data interpretation lies our cutting-edge computer vision transformers. These tools delve into visual data, extracting nuanced insights from imagery. Whether it's discerning building attributes from satellite imagery or identifying intricate components of urban infrastructure, our transformers convert visual cues into quantifiable data.
Predictive AI Analytics: Once visual data is converted into quantifiable metrics, our predictive algorithms weave these into the broader urban tapestry. They identify patterns, forecast trends, and detect anomalies, offering a comprehensive view of the urban fabric.
Adaptive Learning: Urban landscapes are ever-evolving, and so are our models. With each new data intake, they recalibrate, ensuring alignment with the dynamic landscape.
Foundation Models: Deciphering the World's Complexity
With the computational strength of advanced AI algorithms, our models dissect satellite and aerial imagery layer by layer. They not only identify individual elements but also understand their contextual relevance:
Infrastructure Mapping: From tracing the unique electrical footprints of substations to understanding the layouts of distribution lines, our models capture the infrastructural setups.
Interconnected Analysis: Our models understand the symbiotic relationships between different infrastructure components. For instance, they can anticipate the cascade effect of a power outage in one substation on surrounding neighborhoods and businesses.
This deep-dive approach is more than just technology; it's our commitment to transforming the intricate and often overlooked details of our world into precise, actionable, and forward-looking insights.
Scaling resilience modeling with ML Operations and Catastrophe Modeling
AI Operations: Scaling the Digital Frontier
Creating and maintaining an extensive digital twin entails far more than relying on advanced AI models. This intricate process involves orchestration, optimizing efficiency within expansive datasets, and seamless adaptation to a rapidly evolving landscape. We leverage cutting-edge Machine Learning (ML) operations, which encompass large-scale pipelines, comprehensive model versioning, and robust data warehousing, with monitoring and observability. This innovative approach significantly enhances our data science and AI capabilities. Our unwavering dedication ensures that our digital twin remains a perpetually updated, consistently accurate, and dependable representation of reality.
Catastrophe Modeling on steroids
At One Concern, we adopt a comprehensive, scalable framework to evaluate potential damage and subsequent downtime from various hazards. This strategic framework leverages the power of hazard-specific models combined with vulnerability assessments to provide precise damage predictions. Our approach includes:
Flexible catastrophe modeling integration: We are not bound to a particular view of climate change, and have integrations with multiple climate risk organizations to import their view of the future. You can even ‘bring-your-own” hazard model to us!
Damage Prediction Models: Leveraging machine learning for hazard-prone structures and vulnerability models for other types of infrastructure, we anticipate an array of potential damage states along with their associated probabilities.
Recovery Modeling: We use probabilistic fragility functions to generate system-level recovery combining the component-level recovery curves for each dependency (like power substations, or cranes in a port), providing an in-depth view of downtime distribution for each commercial property.
Scenario-Informed Insights: Our models encapsulate climate scenarios such as RCP4.5 and RCP8.5. This meticulous approach yields data-driven insights that help shape customized resilience strategies.