Presenters-0430: Difference between revisions
From CSDMS
(Created page with "{{Presenters temp |CSDMS meeting event title=CSDMS3.0 - Bridging Boundaries |CSDMS meeting event year=2019 |CSDMS meeting presentation type=Clinic |CSDMS meeting first name=Da...") |
No edit summary |
||
| Line 12: | Line 12: | ||
}} | }} | ||
{{Presenters presentation | {{Presenters presentation | ||
|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> | ||
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 youtube code=0 | |CSDMS meeting youtube code=0 | ||
|CSDMS meeting participants=0 | |CSDMS meeting participants=0 | ||
}} | |||
{{Presenters keywords temp | |||
|Presentation keywords=Deep learning methods | |||
}} | |||
{{Presenters keywords temp | |||
|Presentation keywords=Neural networks | |||
}} | |||
{{Presenters keywords temp | |||
|Presentation keywords=Machine Learning | |||
}} | }} | ||
{{Presenters additional material | {{Presenters additional material | ||
|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 | ||
}} | }} | ||
Revision as of 09:44, 26 March 2019
CSDMS3.0 - Bridging Boundaries
Landcover and landform classification using deep neural networks
Abstract
Note: As of March 15th, this clinic is sold out. 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.
Please acknowledge the original contributors when you are using this material. If there are any copyright issues, please let us know (CSDMSweb@colorado.edu) and we will respond as soon as possible.
Of interest for:
