UrbanLC reference documentation#

Introduction#

UrbanLC is a Python library for land cover classification (LCC) from Landsat Images.

It features pretrained deep learning models, which are compatible with all Landsat sensors up-to-date: MSS, TM, and OLI-TIRS. The library further contains some utility functions for analyzing and visualizing land cover maps and tutorials for for researchers and practitioners.

Note

  • This library is developed by Worameth Chinchuthakun and maintained by Global Urban Climate Studies Lab at Tokyo Institute of Technology

Installation#

pip install git+https://github.com/TokyoTechGUC/urbanlc

GitHub link.

Disclaimer#

The performance of models depends on the quality of input data (Landsat surface reflectance), such as the degree of cloud coverage and geometric/radiometric calibration errors. There is no guarantee that the predictions will reflect the actual past land covers. Hence, users are strongly advised to verify their accuracy by comparing/ensembling with other available historical data to increase the plausibility of the hindcasts. With this, the developers are not held responsible for any decisions based on the model.

Table of Content#

API documentation