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|CSDMS meeting abstract presentation=This clinic will introduce deep learning methods for semantic segmentation of fluvial sedimentary landforms and riparian environments, using high-resolution aerial imagery. Deep neural networks are the current state-of-the-art for discrete classification of remotely sensed imagery from Earth observation platforms. The clinic will guide users through the process of preparing training datasets, training models, and evaluation. A number of different deep convolutional neural network architectures for image feature extraction and pixel-scale classifications will be explored and compared. The clinic will use the keras and tensorflow libraries within the python programming language. This hands-on class will be taught using Google colab through a browser, with the materials hosted on github. Participants will require a working knowledge of python. Some working knowledge of machine learning would be helpful, but we will assume no prior experience with machine/deep learning, neural networks, tensorflow, or keras. | |CSDMS meeting abstract presentation=<!--<font color="red">Note: As of March 15th, this clinic is sold out.</font><br> | ||
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This clinic will introduce deep learning methods for semantic segmentation of fluvial sedimentary landforms and riparian environments, using high-resolution aerial imagery. Deep neural networks are the current state-of-the-art for discrete classification of remotely sensed imagery from Earth observation platforms. The clinic will guide users through the process of preparing training datasets, training models, and evaluation. A number of different deep convolutional neural network architectures for image feature extraction and pixel-scale classifications will be explored and compared. The clinic will use the keras and tensorflow libraries within the python programming language. This hands-on class will be taught using Google colab through a browser, with the materials hosted on github. Participants will require a working knowledge of python. Some working knowledge of machine learning would be helpful, but we will assume no prior experience with machine/deep learning, neural networks, tensorflow, or keras. | |||
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|Presentation keywords=Deep learning methods | |||
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|Presentation keywords=Neural networks | |||
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|Presentation keywords=Machine Learning | |||
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|Working group member=Terrestrial Working Group, Coastal Working Group, Cyberinformatics and Numerics Working Group, Hydrology Focus Research Group, Ecosystem Dynamics Focus Research Group, Coastal Vulnerability Initiative, Artificial Intelligence & Machine Learning Initiative | |Working group member=Terrestrial Working Group, Coastal Working Group, Cyberinformatics and Numerics Working Group, Hydrology Focus Research Group, Ecosystem Dynamics Focus Research Group, Coastal Vulnerability Initiative, Artificial Intelligence & Machine Learning Initiative | ||
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Latest revision as of 16:34, 11 June 2025
CSDMS3.0 - Bridging Boundaries
Landcover and landform classification using deep neural networks
Abstract
This clinic will introduce deep learning methods for semantic segmentation of fluvial sedimentary landforms and riparian environments, using high-resolution aerial imagery. Deep neural networks are the current state-of-the-art for discrete classification of remotely sensed imagery from Earth observation platforms. The clinic will guide users through the process of preparing training datasets, training models, and evaluation. A number of different deep convolutional neural network architectures for image feature extraction and pixel-scale classifications will be explored and compared. The clinic will use the keras and tensorflow libraries within the python programming language. This hands-on class will be taught using Google colab through a browser, with the materials hosted on github. Participants will require a working knowledge of python. Some working knowledge of machine learning would be helpful, but we will assume no prior experience with machine/deep learning, neural networks, tensorflow, or keras.
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Of interest for:
