opticallyshallowdeep 1.2.2

Identify optically shallow and deep waters in satellite imagery

Optically-Shallow-Deep

This python tool delineates optically shallow and deep waters in Sentinel-2 imagery. The tool uses a deep neural network (DNN) that was trained on a diverse set of global images.

Supported input includes Level-1C (L1C) SAFE files and ACOLITE-processed L2R netCDF files. The output geotiff contains probabilities of water pixels being optically shallow and deep.

Home page: https://github.com/yulunwu8/Optically-Shallow-Deep

Publication: Richardson, G., Foreman, N., Knudby, A., Wu, Y., & Lin, Y. (2024). Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery. Remote Sensing of Environment, 311, 114302. https://doi.org/10.1016/j.rse.2024.114302

Originally coded by by Galen Richardson and Anders Knudby, modified and packaged by Yulun Wu

Installation

1 - Create a conda environment and activate it:

conda create --name opticallyshallowdeep python=3.10
conda activate opticallyshallowdeep

2 - Install tensorflow

For mac OS:

conda install -c apple tensorflow-deps
python -m pip install tensorflow-macos

For Windows and Linux:

pip3 install tensorflow==2.13.0

More on installing tensorflow: https://www.tensorflow.org/install

3 - Install opticallyshallowdeep:

pip3 install opticallyshallowdeep

Quick Start

For L1C files:

import opticallyshallowdeep as osd

# Input file 
file_L1C = 'folder/S2.SAFE' 

# Output folder 
folder_out = 'folder/test_folder_out'

# Run the OSW/ODW classifier 
osd.run(file_L1C, folder_out)

For ACOLITE L2R files:

import opticallyshallowdeep as osd

# Input files 
file_L1C = 'test_folder_in/S2.SAFE' 
file_L2R = 'test_folder_in/L2R.nc' 

# Output folder 
folder_out = 'folder/test_folder_out'

# Run the OSW/ODW classifier 
osd.run(file_L1C, folder_out, file_L2R=file_L2R)

The L1C file is always required as it contains a built-in cloud mask. Pixels within 8 pixels of the cloud mask are masked to reduce the impact of clouds.

Output is a 1-band geotiff, with values of prediction probability of optically shallow water (OSW): 100 means most likely OSW, 0 means most likely optically deep water (ODW). Non-water pixels are masked. It is recommended to treat pixels between 0 and 40 as ODW, and pixels between 60 and 100 as OSW (Richardson et al., 2024).

A log file, an intermediate multi-band geotiff, and a preview PNG are also generated in the output folder. They can be deleted after the processing.

Sample Sentinel-2 scene and output:

Training, test, and validation data

All annotated shapefiles used in training, testing, and validating the DNN model are in the annotated_shapefiles folder, grouped by Sentinel-2 Scene ID.