khlaifiabilel / in-situ-water-quality-monitoring-in-oil-and-gas-operations Goto Github PK

Salinity and turbidity detection of water ponds using satellite imagery. This is a statistical algorithm to analyze water quality in lakes/ponds/deltas using LandSat8 satellite time series data. The repo contains methods for detection on 2 types of datasets, the Commercial dataset (includes all the bands in a single .TIF file) and LandSat8 sensor dataset (Contains individual .TIF files for each band)

Satish Kumar*, Rui Kou*, Vikram Jayaram

This repository includes:

• Source code for Water Quality detection algorithm
• Dataloader for LandSat8 imagery and Satelytics imager
• Python code for Pansharpening multispectral image using panchromatic band
• Example datasample for generating color coded output, histogram, volume of water in pond/lake/delta etc

The repo structure follows standard dataloader and utility function imported into the main code. Please consider citing our work if it is useful to you

• Python ≥ 3.5
• pip ≥ 21.1.0
• Virtualenv

1. Clone this repository
2. Create a python (3.6 or greater) virtualenv
3. Activate the virtualenv
4. Install dependencies

pip install -r requirements.txt

5. cd to rio-pansharpen directory and run the following commands

cd rio-pansharpen
rm -rf build/*
python setup.py install

This finishes the environment setup for water quality detection

main.py is the primary file to run the code. To see the list of arguments run

python main.py --help

It dumps the following output

usage: main.py [-h] [-d DATA_DIR] [-r REPORT_PATH] [-vo VISUAL_OUT]
               [-ho HIST_OUT] [-ph PLOT_HIST] [-vt VOLUME]

optional arguments:
  -h, --help            show this help message and exit
  -d DATA_DIR, --data_dir DATA_DIR
                        path to data directory
  -r REPORT_PATH, --report_path REPORT_PATH
                        file to get location of ponds
  -vo VISUAL_OUT, --visual_out VISUAL_OUT
                        path to dir for qualitative output
  -ho HIST_OUT, --hist_out HIST_OUT
                        path to directory to dump histogram data
  -ph PLOT_HIST, --plot_hist PLOT_HIST
                        enable histogram plot
  -vt VOLUME, --volume VOLUME
                        enable volume and top 10 average csv file

Sample commands to run the code

python main.py -d <path to data directory> -ph <True/False> -vt <True/False>

The output of above command will generate color coded output of water quality of each pond/lake in visual_output directory and histogram plot in histogram directory. Along with that, it will generate 2 .csv files with names vol.csv and top5avg.csv, which contains the relative volumne of water in each pond and expected value of top-10 pixel of water quality output respectively