Satsense is an open source Python library for patch based land-use and land-cover classification, initially developed for a project on deprived neighborhood detection. However, many of the algorithms made available through Satsense can be applied in other domains, such as ecology and climate science.

Satsense is based on readily available open source libraries, such as opencv for machine learning and the rasterio/gdal and netcdf libraries for data access. It has a modular design that makes it easy to add your own hand-crafted feature or use deep learning instead.

Detection of deprived neighborhoods is a land-use classification problem that is traditionally solved using hand crafted features like HoG, Lacunarity, NDXI, Pantex, Texton, and SIFT, computed from very high resolution satellite images. One of the goals of Satsense is to facilitate assessing the performance of these features on practical applications. To achieve this Satsense provides an easy to use open source reference implementation for these and other features, as well as facilities to distribute feature computation over multiple cpu’s. In the future the library will also provide easy access to metrics for assessing algorithm performance.

• 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.

Can be found on readthedocs.

Please see the installation guide on readthedocs.

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.