added figures

Author: ssmahto

README.rst

@@ -14,7 +14,7 @@ It built on top of `GDAL <https://gdal.org/>`_, `Scikit-Learn <https://scikit-le
 and other popular python packages. ``InfeRes`` is developed with a novel algorithm which helps inferring reservoir characteristics even from the partially cloudy images.
 ``InfeRes`` can be applied to monitor water surface area in any reservoir or waterbody; whereas, storage-volume can be obtained for the reservoirs build only after 2000 (limitation of DEM acquisition).
 
-For more information, see the full `documentation <https://inferes-test.readthedocs.io/en/latest/>`_, or `GitHub <https://github.com/ssmahto/InfeRes_test>`_.
+For more information, see the full `documentation <https://inferes-test.readthedocs.io/en/latest/>`_, or `GitHub <https://github.com/Critical-Infrastructure-Systems-Lab/InfeRes>`_.
 
 
 Currently ``InfeRes`` is only tested on Python 3.8.

paper/paper.md

@@ -45,7 +45,7 @@ Motivated by these modelling challenges, we sought to develop a package that cou
 
 # Functionality
 
-`InfeRes` is available on GitHub (https://github.com/Critical-Infrastructure-Systems-Lab/InfeRes). Its documentation (https://inferes-test.readthedocs.io/en/latest/index.html) provides a detailed explanation of the installation steps and guidelines for running the code. This includes the preparation of the required modules, which can be easily imported in the Python environment via the pip, conda, or conda-forge package manager.
+`InfeRes` is available on `GitHub <https://github.com/Critical-Infrastructure-Systems-Lab/InfeRes>`. Its `documentation <https://inferes-test.readthedocs.io/en/latest/index.html>` provides a detailed explanation of the installation steps and guidelines for running the code. This includes the preparation of the required modules, which can be easily imported in the Python environment via the pip, conda, or conda-forge package manager.
 
 The package's core functionality is divided into two main modules, which are run in sequence. The first module (`data_download.py`) downloads the Landsat imageries using the Earth Engine Python API. To that purpose, the user needs to install the *earthengine-api* package and authenticate with the Google Earth Engine account. In principle, the user can download any set of data from the Google Earth Engine using `data_download.py`; however, for the current objective, we simply use the Normalised Difference Water Index (NDWI) and Quality Assessment Bands (QA_PIXEL) from the Landsat data collection. Alternatively, the user can also download the GREEN and NIR bands to calculate NDWI, instead of downloading NDWI directly from the Earth Engine. Note that more storage and time would be required in such case. The data download module also helps users change the data specifications, such as satellite sensor, area of interest, spatial resolution, and selection of bands. The time needed for data download depends on the size of the Landsat images. For instance, when tested on one of the biggest reservoir area (3010 x 5413 pixels of 30 m resolution), it took around 6-8 hours to download 1330 Landsat images (Landsat 5, 7, and 8), which required nearly 45 GB of storage.