Novel Geospatial Data Science Techniques for Interdisciplinary Applications

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dc.contributor Vrbsky, Susan
dc.contributor Hong, Xiaoyan
dc.contributor Gan, Yu
dc.contributor Pu, Lina
dc.contributor.advisor Jiang, Zhe Sainju, Arpan Man
dc.contributor.other University of Alabama Tuscaloosa 2021-11-23T14:33:52Z 2021-11-23T14:33:52Z 2021
dc.identifier.other u0015_0000001_0003895
dc.identifier.other Sainju_alatus_0004D_14574
dc.description Electronic Thesis or Dissertation en_US
dc.description.abstract With the advancement of GPS and remote sensing technologies, an enormous amount of geospatial data are being collected from various domains. Examples include crime locations, road temporally detailed networks, earth observation imagery, and GPS trajectories. Geospatial data science studies computational techniques to discover interesting, previously unknown, but potentially useful patterns from large spatial datasets. It is important for various applications. Crime hotspot detection helps law enforcement departments to create effective strategies to allocate police resources and to prevent crimes. Earth observation imagery classification plays a crucial role in flood extent mapping and water resource management. Big companies like UPS use truck GPS trajectories data to find efficient routes that can ultimately minimize the delivery time and reduce carbon footprint. However, geospatial data science poses several computational challenges. First, the spatial data volume is rapidly growing. For example, NASA collects around 12TB of earth observation imagery per day. Second, spatial data exhibits spatial dependency which imply nearby samples are not statistically independent. Third, different spatial patterns of interest may exist in different spatial scales. Finally, there can be limited observations. For example, sometimes it can be difficult or even impossible to get the complete observation of spatial features in an area due the presence of obstacles (e.g., clouds). My thesis investigates novel geospatial data science techniques to address some of these challenges. I propose novel parallel spatial colocation mining algorithms on GPUs to address the challenge of large data volume. Similarly, I propose a deep learning framework to automatically map the road safety features from streetview imagery that captures spatial dependency. Next, I propose a novel approach to address the challenge of limited observation based on the physics-aware spatial structural constraint. Finally, I propose a novel spatial structured model called hidden Markov contour tree (HMCT), a contour tree structure, to capture directed spatial dependency on flow directions between all locations on a 3D surface. en_US
dc.format.medium electronic
dc.format.mimetype application/pdf
dc.language English
dc.language.iso en_US
dc.publisher University of Alabama Libraries
dc.relation.ispartof The University of Alabama Electronic Theses and Dissertations
dc.relation.ispartof The University of Alabama Libraries Digital Collections
dc.relation.hasversion born digital
dc.rights All rights reserved by the author unless otherwise indicated. en_US
dc.subject Colocation Mining
dc.subject Deep Learning
dc.subject Flood
dc.subject GPU
dc.subject Machine Learning
dc.subject Traffic Safety
dc.title Novel Geospatial Data Science Techniques for Interdisciplinary Applications en_US
dc.type thesis
dc.type text University of Alabama. Department of Computer Science Computer Science The University of Alabama doctoral Ph.D.

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