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
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 CONTRIBUTING.md for our contribution guidelines.
If you use Satsense for scientific research, please cite it. You can download citation files from research-software.nl.
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.
The test data has been extracted from the Copernicus Sentinel data 2018