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This two-part clinic will introduce deep l … This two-part clinic will introduce deep learning methods for semantic segmentation of high-resolution aerial imagery for the purposes of landuse/cover/form classification. The datasets we will use consist of images of shoreline environments, with a focus on general-purpose classification in terrestrial, fluvial and coastal ecology and geomorphology.<br><br>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.<br><br>Both the concepts and specific software would apply to many similar classification tasks at landscape scales. This clinic is composed of two, 2-hr sessions. You should sign up to both; the first clinic introduces the topic, data, and technology we use to solve the problem, and the second clinic implements these ideas and evaluates the results.</br></br></br>'''Clinic materials can be found at:'''</br>* https://mardascience.gitlab.io/deep_learning_landscape_classification</br>* https://colab.research.google.com/drive/1krjeCmKoAng0BWy-4mzHVX-eAqQ9qy22?usp=sharing</br>* https://colab.research.google.com/drive/1_ddXkrZCRne7qJ2RXHV5l3qOnk98KIyp?usp=sharingrch.google.com/drive/1_ddXkrZCRne7qJ2RXHV5l3qOnk98KIyp?usp=sharing +
daniel@mardascience.com +
CSDMS 2020: Linking Ecosphere and Geosphere +
Daniel +
Marda Science +
Buscombe +
Clinic +
Arizona +
Part I: Landcover and landform classification using deep neural networks +
eHikjEJ6D2M +
United States +
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21:57:20, 9 September 2019 +
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22:34:14, 11 June 2025 +
Terrestrial Working Group +, Coastal Working Group +, Marine Working Group +, Hydrology Focus Research Group +, Chesapeake Focus Research Group +, Human Dimensions Focus Research Group +, Ecosystem Dynamics Focus Research Group +, Coastal Vulnerability Initiative + and Artificial Intelligence & Machine Learning Initiative +
