A curated list of Google Earth Engine resources. Please visit the Awesome-GEE GitHub repo if you want to contribute to this project.
- Earth Engine official websites
- Get Started
- JavaScript API
- Python API
- R
- QGIS
- Apps
- Presentations
- Videos
- Projects
- Websites
- Datasets
- Papers
- Contributing
- License
- Official homepage
- JavaScript Code Editor
- API Documentation
- Data Catalog
- Timelapse
- Earth Engine Apps
- Blog
- Sign up
- Developers Forum
- Issue Tracker
- Earth Engine API on GitHub
- Sign up for an Earth Engine account.
- Read the Earth Engine API documentation - Get Started with Earth Engine.
- Read another Earth Engine API documentation - Client vs. Server. Make sure you have a good understanding of client-side objects vs server-side objects.
- Try out the JavaScript API or Python API (e.g., geemap).
- JavaScript Code Editor - The official Google Earth Engine JavaScript Code Editor.
- earthengine-api - The official Google Earth Engine Python API.
- geemap - A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets.
- geeadd - Google Earth Engine Batch Asset Manager with Addons.
- earthengine-py-notebooks - A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping.
- earthengine-py-examples - A collection of 300+ examples for using Earth Engine and the geemap Python package.
- ee-tensorflow-notebooks - Repository to place example notebooks for Deep Learning applications with TensorFlow and Earth Engine.
- rgee - An R package for using Google Earth Engine.
- Earth Engine QGIS Plugin - Integrates Google Earth Engine and QGIS using Python API.
- qgis-earthengine-examples - A collection of 300+ Python examples for using Google Earth Engine in QGIS.
- Using the geemap Python package for interactive mapping with Earth Engine - Earth Engine Virtual Meetup on May 8, 2020
- Cloud computing and interactive mapping with Earth Engine and open-source GIS - GeoInsider webinar on May 28, 2020
- Mapping Wetland Inundation Dynamics using Google Earth Engine - Machine learning and data fusion workshop on June 10, 2020
- geemap tutorials on YouTube
- geemap tutorials on 哔哩哔哩
- geemap tutorials on 西瓜视频
- GeoInsider webinar on GEE
- Google Earth Engine on Research Gate
- Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
- Pekel, J.-F., Cottam, A., Gorelick, N., Belward, A.S., 2016. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422. https://doi.org/10.1038/nature20584
- Yamazaki, D., Trigg, M.A., 2016. Hydrology: The dynamics of Earth’s surface water. Nature. https://doi.org/10.1038/nature21100
- Wu, Q., Lane, C.R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H.E., Lang, M.W., 2019. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sens. Environ. 228, 1–13. https://doi.org/10.1016/j.rse.2019.04.015
- Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K., Ziegler, A.D., Chen, A., Gong, P., Chen, J., Hu, G., Chen, Y., Wang, S., Wu, Q., Huang, K., Estes, L., Zeng, Z., 2020. High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability 1–7. https://doi.org/10.1038/s41893-020-0521-x
- Li, X., Zhou, Y., Meng, L., Asrar, G.R., Lu, C., Wu, Q., 2019. A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States. Earth System Science Data. 11(2), 881-894. https://doi.org/10.5194/essd-11-881-2019
Contributions welcome! Read the contribution guidelines first.
To the extent possible under law, Qiusheng Wu has waived all copyright and related or neighboring rights to this work.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent