Learn how to create a custom LULC dataset by combining multispectral satellite and vector data.
Author(s):
- Sambhav Singh Rohatgi, ML engineer and researcher at SpaceSense, sambhav@spacesense.ai
- Anthony Mucia, Remote Sensing scientist at SpaceSense, tony@spacesense.ai
Originally presented at NeurIPS 2022
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Estimated time to execute end-to-end: 35 minutes
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.
Rohatgi, S. S., & Mucia, A. (2022). Automating the Creation of LULC Datasets for Semantic Segmentation [Tutorial]. In Conference on Neural Information Processing Systems. Climate Change AI. https://doi.org/10.5281/zenodo.11619139
@misc{rohatgi2022automating,
title={Automating the Creation of LULC Datasets for Semantic Segmentation},
author={Rohatgi, Sambhav Singh and Mucia, Anthony},
year={2022},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.11619139},
booktitle={Conference on Neural Information Processing Systems},
howpublished={\url{https://github.com/climatechange-ai-tutorials/lulc-semantic-segmentation}}
}