Land Use and Land Cover (LULC) Classification using Deep Learning

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

Access this tutorial

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

Contribute to this tutorial

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.

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License

Usage of this tutorial is subject to the MIT License.

Cite

Plain Text

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

BibTeX

@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}
}