ORCID
0000-0002-2134-8108
Keywords
Informatics, UAV, Remote Sensing, Computer Vision, Multi-Source Data Fusion
Abstract
Environmental degradation, manifested through stormwater runoff, waste accumulation, green-house gas emissions, and infrastructure deterioration, poses escalating risks to both built and natural systems. Traditional monitoring approaches are often constrained by limited spatial coverage, slow response times, and insufficient resolution, making them inadequate for capturing the complexity and rapid dynamics of modern urban environments. Accelerating urbanization further intensifies these pressures by increasing resource demand and situating human communities closer to environmentally vulnerable areas. In response to these challenges, this dissertation advances the field of environmental informatics through the development of UAV-based, computer vision–enabled frameworks for quantitative assessment of environmental degradation. The first research thrust introduces a multimodal UAV data fusion framework that combines LiDAR and multispectral imagery to detect post-hurricane water ponding and surface degradation. The second component advances volume estimation through a cascaded, GCP-free raster registration method that uses mutual information to improve the spatial alignment and accuracy of UAV-derived elevation models. The third contribution develops an automated methane leakage localization approach by integrating UAV-based CH4 measurements with an inverse Gaussian dispersion model to estimate emission intensities and source locations. The final component synthesizes current odor monitoring practices, highlighting the limitations of traditional point-based methods and demonstrating the potential of robotic platforms and AI-driven analytics for scalable, spatiotemporal air quality assessment. These contributions establish an adaptable and intelligent UAV-based informatics framework that advances automated, data-driven environmental monitoring, supporting the development of smarter, more resilient, and sustainable urban systems.
Completion Date
2025
Semester
Fall
Committee Chair
Dr. Peng "Patrick" Sun
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Civil, Environmental and Construction Engineering
Format
Release Date
12-15-2026
Document Type
Dissertation
Campus Location
Orlando (Main) Campus
Subjects
Environmental engineering--Remote sensing; Urban ecology--Remote sensing; Urban hydrology--Research; Pollution--remote sensing; Environmental management--Remote sensing
STARS Citation
Hassan, Syed Zohaib, "Towards Automated Assessment of Built Environment using UAV, Computer Vision, and Multi-Source Data Fusion" (2025). Graduate Thesis and Dissertation post-2024. 530.
https://stars.library.ucf.edu/etd2024/530
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