Satsense is a Python library for land use/cover classification using satellite imagery
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Satsense is a library for land use/cover classification using satellite imagery.

  • satsense - library for analysing satellite images, performance evaluation, etc.
  • notebooks - IPython notebooks for illustrating and testing the usage of Satsense

We are using python 3.6/3.7 and jupyter notebook for our code.

Installation from github

Assuming you have conda installed and are in the directory where you have checked out this repository, you can install the dependencies by running:

conda create --name satsense python=3
conda activate satsense
conda env update

If you prefer to use the package manager of your OS, use it to install the GDAL and netCDF4 dependencies. On Ubuntu Linux 18.04 and later, you can do so by running

sudo apt install libgdal-dev libnetcdf-dev

When using your OS's package manager, you may still want to create and activate a virtual environment for satsense, e.g. using venv

python3 -m venv ~/venv/satsense
source ~/venv/satsense/bin/activate

Finally, to install satsense, run

pip install .


Contributions are very welcome! Please see for our contribution guidelines.

Citing Satsense

If you use Satsense for scientific research, please cite it. You can download citation files from


The collection of algorithms made available trough this package is inspired by

J. Graesser, A. Cheriyadat, R. R. Vatsavai, V. Chandola, J. Long and E. Bright, "Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 4, pp. 1164-1176, Aug. 2012. doi: 10.1109/JSTARS.2012.2190383

Jordan Graesser himself also maintains a library with many of these algorithms.

Test Data

The test data has been extracted from the Copernicus Sentinel data 2018