Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model to classify satellite images into 10 LULC categories.
Authors: Isabelle Tingzon and Ankur Mahesh
Originally presented at Climate Change AI Summer School 2022
We recommend executing these notebooks in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Part 1:
Part 2:
Estimated time to execute end-to-end: 1 hour
Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.
Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Tingzon, I., & Mahesh, A. (2024). Land Use and Land Cover (LULC) Classification using Deep Learning [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.11584954
@misc{tingzon2024land,
title={Land Use and Land Cover (LULC) Classification using Deep Learning},
author={Tingzon, Isabelle and Mahesh, Ankur},
year={2024},
howpublished={\url{https://github.com/climatechange-ai-tutorials/lulc-classification}},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.11584954},
booktitle={Climate Change AI Summer School}
}