Landscape mapping using remote sensing and neural networks
Develop high-resolution maps of Arctic vegetation using machine learning and satellite imagery (e.g., NASA)
Remote sensing products were combined and maps of vegetation distribution evaluated against field data for the Seward Peninsula
A convolutional neural network (CNN) approach produced highly accurate vegetation classifications. Hyper-spectral datasets (e.g., AVIRIS) were most useful for our machine learning approaches. Accurate and high-resolution datasets generated using our approach are needed for Arctic models.
Oak Ridge National Laboratory