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giswqs / Awesome-GEE

  • четверг, 18 июня 2020 г. в 00:23:39
https://github.com/giswqs/Awesome-GEE


A curated list of Google Earth Engine resources



Awesome Earth Engine Awesome

A curated list of Google Earth Engine resources. Please visit the Awesome-GEE GitHub repo if you want to contribute to this project.

Table of Contents

Earth Engine official websites

Get Started

  1. Sign up for an Earth Engine account.
  2. Read the Earth Engine API documentation - Get Started with Earth Engine.
  3. Read another Earth Engine API documentation - Client vs. Server. Make sure you have a good understanding of client-side objects vs server-side objects.
  4. Try out the JavaScript API or Python API (e.g., geemap).
  5. Read Coding Best Practices.

Get Help

JavaScript API

Playground

Tutorials

Repositories

Python API

Installation

Packages

  • 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.
  • geeup - Simple CLI for Google Earth Engine Uploads.
  • cartoee - Publication quality maps using Earth Engine and Cartopy.
  • gee_tools - A set of tools for working with Google Earth Engine Python API.
  • landsat-extract-gee - Get Landsat surface reflectance time-series from google earth engine.
  • Ndvi2Gif - Creating seasonal NDVI compositions GIFs.

Repositories

  • earthengine-py-notebooks - A collection of 360+ Jupyter 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.
  • CoastSat - Global shoreline mapping tool from satellite imagery.

R

Packages

  • rgee - An R package for using Google Earth Engine.
  • earthEngineGrabR - Simplify the acquisition of remote sensing data.

Repositories

  • rgee-examples - A collection of 250+ examples for using Google Earth Engine with R.

Tutorials

QGIS

  • Earth Engine QGIS Plugin (Website, GitHub) - 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.

GitHub Developers

Community

Individuals

Twitter

Bots

Google affiliated

Individuals

Apps

Presentations

geemap

Videos

Google

General

  • Getting Started with Earth Engine with Sabrina Szeto (video - slides)
  • Earth Engine Virtual Meetup on May 6, 2020 (video)

geemap

Projects

Websites

Datasets

Landsat

Sentinel

NAIP

Land Cover

Papers

Highlights

  • 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

Review

  • Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
  • Kumar, L., Mutanga, O., 2018. Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sensing 10, 1509. https://doi.org/10.3390/rs10101509

Hydrology

  • 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
  • Donchyts, G., Baart, F., Winsemius, H., Gorelick, N., Kwadijk, J., van de Giesen, N., 2016. Earth’s surface water change over the past 30 years. Nat. Clim. Chang. 6, 810. https://doi.org/10.1038/nclimate3111
  • 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

Urban

  • 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., Zhu, Z., Cao, W., 2020. A national dataset of 30 m annual urban extent dynamics (1985–2015) in the conterminous United States. Earth System Science Data 12, 357. https://doi.org/10.5194/essd-12-357-2020

Vegetation

  • 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

Agriculture

  • Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., Gorelick, N., 2017. Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sensing 9, 1065. https://doi.org/10.3390/rs9101065
  • Xiong, J., Thenkabail, P.S., Gumma, M.K., Teluguntla, P., Poehnelt, J., Congalton, R.G., Yadav, K., Thau, D., 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. Remote Sens. 126, 225–244. https://doi.org/10.1016/j.isprsjprs.2017.01.019

Land Cover

  • Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., Liang, S., 2020. Annual Dynamics of Global Land Cover and its Long-term Changes from 1982 to 2015. Earth Syst. Sci. Data. 12, 1217–1243. https://doi.org/10.5194/essd-12-1217-2020
  • Carrasco, L., O’Neil, A.W., Morton, R.D., Rowland, C.S., 2019. Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sensing 11, 288. https://doi.org/10.3390/rs11030288

Disaster Management

  • DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J.W., Lang, M.W., 2020. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sens. Environ. 240, 111664. https://doi.org/10.1016/j.rse.2020.111664
  • Liu, C.-C., Shieh, M.-C., Ke, M.-S., Wang, K.-H., 2018. Flood Prevention and Emergency Response System Powered by Google Earth Engine. Remote Sensing 10, 1283. https://doi.org/10.3390/rs10081283

Contributing

Contributions welcome! Read the contribution guidelines first.

License

CC0

To the extent possible under law, Qiusheng Wu has waived all copyright and related or neighboring rights to this work.