Climate-Land Cover data used by DeepEcoClimate
Description
Sourced and processed from - Climate normals: CRU TS v4.09 (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.09/) - Land cover: MCD12C1 (https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd12c1-061) - Elevation: GMTED2010 (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-multi-resolution-terrain-elevation) train_data.nc and valid_data.nc are used in TorchModel.py (in the GitHub repo) Check the GitHub repo (https://github.com/peace-Van/ClimViz) for details
Files
Steps to reproduce
In short, DeepEcoClimate is derived in four steps (first three steps in MATLAB): - A neural network trained to map climate normal data to land cover - Land cover related climate features obtained from an intermediate layer of the network, PCA-ed and a self-organizing map trained in the reduced feature space - Analyze the SOM structure, manually merge clusters - Migrate to PyTorch via knowledge distillation Details may be revealed in a publication later.