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Full-Text Articles in Physical Sciences and Mathematics
Glacier Segmentation From Remote Sensing Imagery Using Deep Learning, Bibek Aryal
Glacier Segmentation From Remote Sensing Imagery Using Deep Learning, Bibek Aryal
Open Access Theses & Dissertations
Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. In recent years, remote sensing imagery has been preferred over riskier and resource-intensive field visits for tracking landscape level changes like glaciers. However, periodic manual labeling of glaciers over a large area is not feasible due to the considerable amount of time it requires while automatic segmentation of glaciers has its own set of challenges. Our work aims to study the challenges associated with segmentation of glaciers from remote sensing imagery …
Oil Particle Analysis Using Machine Learning And Holography Imaging, Daniel Cruz
Oil Particle Analysis Using Machine Learning And Holography Imaging, Daniel Cruz
Open Access Theses & Dissertations
Holographic cameras show potential as a sensor to monitor oil spills. Holographic cameras record the light interference from particles in a volume of space, producing an image called a hologram. Processing these holograms is known as hologram reconstruction. It produces a representation of particles located in three-dimensional space. These cameras can record precise shapes and sizes of particles in a volume of water. However, it is very time-consuming and resource-intensive to process the images. Most algorithms that perform particle analysis require the hologram reconstruction step. The well-documented hybrid method is one such algorithm. Machine learning is one possible technique that …
Meteorological Characteristics Of Fog Events In Korean Smart Cities And Machine Learning Based Visibility Estimation, Jaemin Kim, Seung Hee Kim, Hyun Woo Seo, Yi Victor Wang, Yun Gon Lee
Meteorological Characteristics Of Fog Events In Korean Smart Cities And Machine Learning Based Visibility Estimation, Jaemin Kim, Seung Hee Kim, Hyun Woo Seo, Yi Victor Wang, Yun Gon Lee
Institute for ECHO Articles and Research
To address various urban issues such as fine dust, traffic congestion, and water shortage caused by rapid urbanization, a national pilot Smart City is planned in two Korean cities, Sejong and Busan. As weather data is crucial for improving the environment and operating future transportation while constructing a smart city, preparing for future weather disasters by analyzing the characteristics of various meteorological phenomena in the planned development area is necessary. This study analyzed the fog generation characteristics for the period of 2016–2020 at the automatic weather system sites of the Korea Meteorological Administration in Sejong and Busan, and the characteristics …
The Burning Bush: Linking Lidar-Derived Shrub Architecture To Flammability, Michelle S. Bester
The Burning Bush: Linking Lidar-Derived Shrub Architecture To Flammability, Michelle S. Bester
Graduate Theses, Dissertations, and Problem Reports
Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, …