sentinel_sat_downloader

This allows someone to download images from a csv from ESA's Sentinel 2 Scihub

Downloading_process

Contains 4 functions in 1_sentinel_sat_downloader/1_sentinel_sat_downloader.ipynb used to download the Sentinel 2 data from ESA Datahub given a set of CSV files

• Directory Function
  • Gets the names of a directory
• Cleans CSV lists Function
  • cleans/splits df of list of images
• Real Name Function
  • function to help find real names of files
• Search string Function
  • searchs esa datahub for image and downsloads it
• Main function
  • Combination of the 4 functions

DSEN2_process

Contains the code to run Dsen2 with and without original bands on Sentinel 2 images

• DSEN2_process/dsen_test.ipynb (without original bands)
• DSEN2_process/dsen_test-Copy1.ipynb (with original bands)

DSEN2_S2LP_process

Contains 4 functions in DSEN2_S2LP_process/6_DSEN2_application/6_DSEN2_application.ipynb used to automate running DSen2 on Sentinel 2 images

• Directory Function
  • Gets the names of a directory
• Unzipping Function
  • "unzip the files in the directory
• Moving zipped file function
  • Moving zip files to another location
• Three methods to run Dsen2 include:
  • Method 1: Typical Ipython method (working)
  • Method 2: Pass string to cmd inside python (not working yet)
  • Method 3: Pass string to run (working)
    • ipython_command_maker function

Estimate_prediction_process

Contains 2 methods to estimate uncertainity

• auto_examples_jupyter folder (Sklearn/Forestci method examplars)
  • plot_mpg_svr.ipynb - Plotting Bagging Regression Error Bars
  • plot_mpg.ipynb - Plotting Regression Forest Error Bars
  • plot_spam.ipynb - Plotting Classification Forest Error Bars
• Tensorflow_probability folder (Tensorflow Probability method)
  • A_Tour_of_TensorFlow_Probability.ipynb - General Tensorflow probability examplar
  • Modelling_uncertainty_examplar.ipynb - Modelling Aleatoric and Epistemic uncertainity in a polynomial regression Tensorflow probability examplar
  • Modelling_uncertainity_multi_variable - Modelling Aleatoric and Epistemic uncertainity in a multivariate regression Tensorflow probability examplar
  • Probabilistic_Layers_Regression.ipynb - Modelling Aleatoric and Epistemic uncertainity in a linear regression Tensorflow probability examplar
  • regression.ipynb - Linear regression and Regression with a deep neural network using for single and multiple inputs
  • tensorflow_example.ipynb - An example of Deep Learning Models, Advanced Model Features and methods to Better Model Performance
• Uncertanity_example folder ( examplar of both Sklearn/Forestci and Tensorflow Probability methods on LAI data
  • Uncertainty_Tensorflow_and_Tensorflow_probability.ipynb - The examplar (to be used in NEON)
  • Tensorflow_probability_Detailed_Examples.ipynb - More Tensorflow probability exampes for the examplar (to be used in NEON)