Advancing Optimal Transport and Denoising Diffusion in Generative AI for
Learning in Urban Monitoring Systems
Advisors: Professor Christopher Tessum and Professor Volodymyr Kindratenko
Abstract:
Generative AI, notably through optimal transport and denoising diffusion models (DDM), is
enhancing the synthesis and analysis of data in urban monitoring systems. This technology is
crucial for creating high-fidelity simulations and models that predict dynamics, supporting the
development of resilient infrastructures and optimized processes in the face of operational and
environmental challenges.
Optimal transport theory plays a pivotal role in learning traffic data distributions and enhancing
urban surveillance capabilities. The novel Iterative Generative Adversarial Networks for
Imputation (IGANI) architecture introduces a new approach to data completion using an iterative
GAN leveraged by optimal transport. Additionally, optimal transport theory is applied to
rigorously evaluate one-shot generative models for data augmentation, focusing on
radiofrequency (RF) signal patterns in proliferating Unmanned Aerial Vehicles (UAV). Such
one-shot generative distribution matching enhances RF-based UAV identification in data-limited
environments, enabling more reliable urban surveillance.
DDMs have also emerged as powerful generative tools in computer vision and scientific
machine learning. Prompt-guided outpainting DDMs are explored for generating contextually
rich images for traffic monitoring and expanding datasets with automatic annotations.
AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
(AIDOVECL) reduces the need for manual labeling and improves performance metrics for
vehicle detection. Moreover, a novel approach has been proposed to capture the dynamics of
physical processes using DDMs. This new algorithm for DDM training and sampling
demonstrates its capability to simulate phenomena such as heat distribution, presenting
potential for enhancing efficient environmental planning and management.
Overall, this dissertation illustrates how generative AI, particularly through optimal transport and
DDMs, may address data scarcity and improve the fidelity and utility of synthetic data in urban
monitoring. The implementations presented highlight potentials in data-driven decision-making
within infrastructure systems for real-world scenarios. This work contributes to insights and
methodologies that could inspire further research and practical applications in urban monitoring
and beyond.