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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Identifying Smokestacks In Remotely Sensed Imagery Via Deep Learning Algorithms, Kenneth Moss
Identifying Smokestacks In Remotely Sensed Imagery Via Deep Learning Algorithms, Kenneth Moss
Masters Theses
Locating smokestacks in remote sensing imagery is a crucial first step to calculating smokestack heights, which allows for the accurate modeling of dioxin pollution spread and the study of resulting health impacts. In the interest of automating this process, this thesis examines deep learning networks and how changes in input datasets and network architecture affect image detection accuracy. This initial image detection serves as the first step in automated object recognition and height calculation. While this is applicable to general land use classification, this study specifically addresses detecting smokestack images. Different dataset scenarios are generated from the massive Functional Map …
Southwest Pacific Tropical Cyclone Frequency And Intensity Related To Observed And Modeled Geophysical And Aerosol Variables, Rupsa Bhowmick
Southwest Pacific Tropical Cyclone Frequency And Intensity Related To Observed And Modeled Geophysical And Aerosol Variables, Rupsa Bhowmick
LSU Doctoral Dissertations
The dissertation focuses on western region of Southwest Pacific Ocean (SWPO)
basin (135E - 180, and 5S - 35S) tropical cyclone (TC) climatology using observed
and modeled data. The classification-based machine learning approach
identifies the synoptic geophysical and aerosol environment favorable or unfavorable
for TC intensification and intensity change prior to landfall incorporating
observational and satellite data. A multiple poisson regression model with varying
temporal monthly lags was used to build a relationship between the number of
monthly TC days with basin wide average dust aerosol optical depth (AOD), sea
surface temperature (SST), and upper ocean temperature (UOT). This idea …
Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning – A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach., Bienvenu Sedin Massamba
Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning – A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach., Bienvenu Sedin Massamba
LSU Master's Theses
The Antarctic surface snowmelt is prone to the polar climate and is common in its coastal regions. With about 90 percent of the planet's glaciers, if all of the Antarctica glaciers melted, sea levels will rise about 58 meters around the planet. The development of an effective automated ice-sheet snowmelt monitoring system is therefore crucial.
Microwave remote sensing instruments, on the one hand, are very sensitive to snowmelt and can see day and night through clouds, allowing us to distinguish melting from dry snow and to better understand when, where, and for how long melting has taken place. On the …