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This clinic will introduce deep learning m … 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.ng, neural networks, tensorflow, or keras. +
daniel.buscombe@nau.edu +
CSDMS3.0 - Bridging Boundaries +
Daniel +
Northern Arizona University +
Buscombe +
Clinic +
Arizona +
Landcover and landform classification using deep neural networks +
United States +
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23:59:04, 8 January 2019 +
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22:34:13, 11 June 2025 +
