├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 climatechange-ai-tutorials 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Land Use and Land Cover (LULC) Classification using Deep Learning 2 | 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. 3 | 4 | Authors: Isabelle Tingzon and Ankur Mahesh 5 | 6 | Originally presented at Climate Change AI Summer School 2022 7 | 8 | ## Access this tutorial 9 | 10 | We recommend executing these notebooks in a Colab environment to gain access to GPUs and to manage all necessary dependencies. 11 | 12 | Part 1: 13 | Open In Colab 14 | 15 | 16 | Part 2: 17 | Open In Colab 18 | 19 | 20 | Estimated time to execute end-to-end: 1 hour 21 | 22 | ## Contribute to this tutorial 23 | 24 | Please refer to these [GitHub instructions](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project#about-forking) to open a pull request via the "fork and pull request" workflow. 25 | 26 | Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness. 27 | 28 | ## Climate Change AI Tutorials 29 | Check out the [tutorials page](https://www.climatechange.ai/tutorials?) on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change. 30 | 31 | ## License 32 | Usage of this tutorial is subject to the MIT License. 33 | 34 | ## Cite 35 | 36 | ### Plain Text 37 | 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 38 | 39 | ### BibTeX 40 | 41 | ``` 42 | @misc{tingzon2024land, 43 | title={Land Use and Land Cover (LULC) Classification using Deep Learning}, 44 | author={Tingzon, Isabelle and Mahesh, Ankur}, 45 | year={2024}, 46 | howpublished={\url{https://github.com/climatechange-ai-tutorials/lulc-classification}}, 47 | organization={Climate Change AI}, 48 | type={Tutorial}, 49 | doi={https://doi.org/10.5281/zenodo.11584954}, 50 | booktitle={Climate Change AI Summer School} 51 | } 52 | ``` 53 | --------------------------------------------------------------------------------