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SUMMARY:Geospatial Analytics Dissertation Defense: Rebecca Composto
DESCRIPTION:Title: Leveraging satellite imagery for urban flood mapping and damage assessment \nAdvisor: Dr. Mirela Tulbure\, faculty fellow and professor in the Department of Forestry and Environmental Resources \nAbstract: One-eighth of urban areas in the U.S. are at high risk of flooding due to human development and increasing precipitation. Flood maps of past events can help decision-makers recover from past events and prepare for future ones. Satellite imagery is a useful dataset for mapping past flood events; however\, its effectiveness and accuracy are rarely assessed in dense urban areas. This dissertation addresses this gap in urban flooding by (1) developing a satellite-based urban flood model\, (2) assessing satellite-based flood depth estimates\, and (3) calculating the correlation between satellite-based flood maps and building damage. More accurate methods for urban flood mapping will help emergency managers\, city planners\, and residents adjust to the rising risk of flooding.
URL:https://cnr.ncsu.edu/geospatial/event/geospatial-analytics-dissertation-defense-rebecca-composto/
LOCATION:Jordan Hall 5103\, 2800 Faucette Drive\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Geospatial Analytics Dissertation Defense
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DTSTART;TZID=America/New_York:20260508T110000
DTEND;TZID=America/New_York:20260508T120000
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CREATED:20260417T150018Z
LAST-MODIFIED:20260417T150018Z
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SUMMARY:Geospatial Analytics Dissertation Defense: Felipe Sanchez
DESCRIPTION:Title: Geospatial distribution and dynamics of swine diseases in the United States \nAdvisors: Dr. Gustavo Machado\, faculty fellow and associate professor in the Department of Population Health and Pathobiology & Dr. Chris Jones\, faculty fellow and senior research scholar in the Center for Geospatial Analytics \nAbstract: This defense examines how geospatial modeling contributes to understanding swine disease distribution and transmission in the United States by integrating spatial epidemiology\, machine learning\, and population modeling. We first investigate the spatial and spatiotemporal dynamics of porcine reproductive and respiratory syndrome virus transmission to identify the environmental\, demographic\, and network-related factors associated with outbreak risk. We then develop a spatially explicit machine-learning framework using high-resolution aerial imagery to detect swine farms\, classify production types\, and estimate population sizes. Finally\, we use the Pest or Pathogen Spread (PoPS) model to simulate the spread of African swine fever virus under multiple movement restriction scenarios\, quantify the relative contribution of direct and indirect transmission pathways\, and evaluate how incomplete movement restrictions may undermine outbreak control efforts.
URL:https://cnr.ncsu.edu/geospatial/event/geospatial-analytics-dissertation-defense-felipe-sanchez/
LOCATION:Jordan Hall 5103\, 2800 Faucette Drive\, Raleigh\, NC\, 27695\, United States
CATEGORIES:Geospatial Analytics Dissertation Defense
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