A Climate Innovation and Sustainability Community/Unit/Track within [GDG Nairobi](https://gdg.community.dev/gdg-nairobi/).
Table of Contents
About this project we use LULC (Land Use Land Cover) Project description The amount of a region that is covered by forests, wetlands, impermeable surfaces, farmland, and other land and water types is shown by land cover data. Land use demonstrates how humans utilize the landscape, whether for development, conservation, or a combination of the two. diverse land cover types can be used or managed in very diverse ways.
Our Case study is Kisumu East. In order to analyze Earth Observation data, machine learning (ML) is a potent tool. With its built-in tools and simple APIs, Earth Engine enables users to create and use machine learning models for typical use cases. A common ML task is to classify (Classification task) the pixels in satellite imagery into two or more categories. The approach is useful for Land Use Land Cover mapping and other popular applications. These categories can include water bodies, bare lands, croplands, forested areas, developed areas for the case of LULC.
The Machine learning capabilities in the GEE JS code editor remain limited. For example, there is no support for XGBoost, LightGBM, NGBoost, etc. Moreover, the python ecosystem has much more support for training, valdation and hyperparameter tuning. However, for this functionality to be leveraged, data needs to be downloaded locally or stored in Google Drive or Google Cloud Storage to benefit from the Machine learning python ecosystem. Therfore, this package aims to make it easier and faster to download GEE-processed data in a machine learning-ready format.
Features
- Parallel export of images or sparse images (for example, GEDI).
- Export raster values at points or polygons (ee.FeatureCollection).
- Summarise raster data within polygons (ee.FeatureCollections).
- Extract both tabular and Deep Neural Network (DNN) type datasets.
- Support the export of additional formats (TFrecords)
- Download data from GEE based on local shapefiles
- Add more examples for using the package
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Organizing team: GEE Developers-Nairobi geedevsnairobi@gmail.com
Facilitator: Michael Wafula
Project Link: LULC