PLANET/AI4COPERNICUS

This data set provides sample input and output data of the developed PLANET  tool (hyPer Local climAte driven Tool) for the three use cases investigated (Kenya, Papua New Guinea, Puerto Rico.) funded by the AI4COPERNICUS project.  The capabilities of the tool include A Generative Adversarial Network (GAN) that downscales the climate information and a feedforward neural network that classifies the land suitability for specific crops based on the refined input data. There are four steps, each with a dedicated folder:

STEP1: Input data set (seasonal climate data, Reanalysis data, Digital elevation model data). 

STEP2: Downscaled seasonal climate data sets for each region with GANs. Each region is accompanied by the Generator Loss, the  Peak signal to noise ratio (PSNR) in html formats, the output images  during training by the generator (training.png) and a png with the downscaled meteorological fields. 

STEP3: Sample input data sets (FAO STAT) of yield data for maize and NDVI that are imported to a LSTM network for yield prediction. Output are yield prediction in CSV format, the lstm structure in png and the Mean Square Error of  LSTM training over the epochs. Caution is needed as this step is not mandatory to Land Suitability in STEP4  but act as layer to investigate relationships of NDVI with yield prediction. FAO STAT data only provide crude yield data and user input to the model are more appropriate. 

STEP4: Land Suitability input and output data sets. Besides the meteorological data, SoilGrids data are utilised and a Feed forward network makes a classification ranging from not suitable at all to highly suitable. Output for each region include the confusion matrix in png and the suitability in CSV format for each region. The accuracy of the model is found in the file fnn_acc.html and the structure of the model in FNN_structure.png.