Loading…
Missouri Data Science & Informatics Symposium 2024
Attending this event?
Log in to bookmark your favorites and sync them to your phone or calendar.

Add sessions to your personal schedule to register your attendance (Check-In).
← Back to schedule
Student Spotlight - Solaiman Khan - "Development of Geospatial AI and Deep Learning Models to Estimate High-Resolution Multi-Pollutant Ground Air Quality Using Satellite Remote Sensing"
Tuesday October 22, 2024 10:00am - 10:15am CDT
N201 Memorial Union
Title: Development of Geospatial AI and Deep Learning Models to Estimate High-Resolution Multi-Pollutant Ground Air Quality Using Satellite Remote Sensing

Abstract: Air pollution is associated with different health issues, including heart and lung problems and even premature death. The World Health Organization reports an alarming figure nearly 7 million deaths per year worldwide due to exposure to ambient air pollution. The prevalent methodology for monitoring air quality heavily relies on a network of ground-based sensors, which, despite their accuracy, are limited by their sparse distribution and high operational costs. This scarcity and the resultant lack of coverage impede the ability to capture the nuanced spatial and temporal variations in air quality, thereby failing to delineate localized zones of heightened pollution within urban settings.

An alternative approach is modeling ground air quality from satellite imageries. With the advancement of computational technologies and the outstanding performance of deep learning (DL) in statistical modeling, DL is being applied in extracting ground air quality indicators from satellite data. This later approach of air quality modeling, with or without applications of DL, mostly focused on country or regional level as they mostly used low resolution images. In addition, the DL models are usually employed without considering the geographical principles, which are pivotal for accurately reflecting the spatial dynamics of air pollution. This research aims to develop geospatial deep learning models to estimate high resolution ground air quality, i.e., six criteria air pollutants utilizing satellite imageries.

Tuesday October 22, 2024 10:00am - 10:15am CDT
N201 Memorial Union
  Student Spotlight

Attendees (16)


Sign up or log in to save this to your schedule, view media, check-in, leave feedback and see who's attending!

Tweet Share
Share Modal

Share this link via

Or copy link