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Full-Text Articles in Physical Sciences and Mathematics
Pixel-Wise Machine Learning And Deep Learning Methods Implementation On Multi-Class Wildfire Mapping, Mingda Wu
Pixel-Wise Machine Learning And Deep Learning Methods Implementation On Multi-Class Wildfire Mapping, Mingda Wu
Honors Capstones
Wildfires are destructive natural hazards. Artificial Intelligence (AI) has been a trendy topic in recent years due to its powerful applicability. This study focuses on the use of artificial intelligence (AI) in hazard management, specifically in the field of wildfire mapping. Machine learning and Deep learning are two subsets of AI. This study applied pixel-wise machine learning and deep learning methods to do multi-class mapping on two wildfire events in California, USA. The purpose of this research is to demonstrate the usefulness and advantages of using AI in the field of hazard management. The machine learning methods selected are Random …
On Interpreting Eddy Covariance In Small Area Agricultural Situations With Contrasting Site Management., Joel Oetting
On Interpreting Eddy Covariance In Small Area Agricultural Situations With Contrasting Site Management., Joel Oetting
Doctoral Dissertations
This dissertation examined the carbon sequestration potential of a low C:N soil amendment and its incorporation into the soil over a rolling agricultural field. A segmented planar fit was developed to assess and correct the systematic errors the topography introduces on the carbon dioxide fluxes. The carbon dioxide fluxes were then be partitioned into gross primary productivity and soil respiration to understand the influence of the contrasting management practices, using flux variance partitioning. Concomitant with the partitioning, high resolution temporal and spatial scale remote sensing images were interpolated and standardized to conduct hypothesis testing for treatment effects.
Landslide Detection In The Himalayas Using Machine Learning Algorithms And U-Net, Sansar Raj Meena, Lucas Pedrosa Soares, Carlos H. Grohmann, Cees Van Westen, Kushanav Bhuyan, Ramesh P. Singh, Mario Floris, Filippo Catani
Landslide Detection In The Himalayas Using Machine Learning Algorithms And U-Net, Sansar Raj Meena, Lucas Pedrosa Soares, Carlos H. Grohmann, Cees Van Westen, Kushanav Bhuyan, Ramesh P. Singh, Mario Floris, Filippo Catani
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed …
Rapid Mapping Of Landslides In The Western Ghats (India) Triggered By 2018 Extreme Monsoon Rainfall Using A Deep Learning Approach, Sansar Raj Meena, Omid Ghorbanzadeh, Cees J. Van Westen, Thimmaiah Gudiyangada Nachappa, Thomas Blaschke, Ramesh P. Singh, Raju Sarkar
Rapid Mapping Of Landslides In The Western Ghats (India) Triggered By 2018 Extreme Monsoon Rainfall Using A Deep Learning Approach, Sansar Raj Meena, Omid Ghorbanzadeh, Cees J. Van Westen, Thimmaiah Gudiyangada Nachappa, Thomas Blaschke, Ramesh P. Singh, Raju Sarkar
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the …