【Earth 】真棒的地球引擎资源 | 开发者人工智能社区 --开发者开聊

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2 June 2022

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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

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).
Read Coding Best Practices.

Get Help

JavaScript API

Playground

JavaScript Code Editor - The official Google Earth Engine JavaScript Code Editor.

Repositories

jdbcode/Snazzy-EE-TS-GIF - Apps for creating Landsat time series animations.
fitoprincipe/geetools-code-editor - A set of tools to use in Google Earth Engine JavaScript Code Editor.
Fernerkundung/EarthEngine_scripts - Scripts and snippets for Google Earth Engine.
Google Earth Engine Toolbox (GEET) - Library to write small EE apps or big/complex apps with a lot less code.
LandTrendr - Spectral-temporal segmentation algorithm.
zecojls/tagee - Terrain Analysis in Google Earth Engine (TAGEE).
ee-palettes - A module for generating color palettes in Earth Engine to be applied to mapped data.
gee-ccdc-tools - A suite of tools designed for continuous land change monitoring in Google Earth Engine.
Continuous Degradation Detection (CODED) - A system for monitoring forest degradation and deforestation.
LT-GEE - Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm.

Tutorials

Python API

Installation

Earth Engine 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.
eemont - A Python package that extends the Google Earth Engine Python API with pre-processing and processing tools.
hydra-floods - An open source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data.
restee - A package that aims to make plugging Earth Engine computations into downstream Python processing easier.
wxee - A Python interface between Earth Engine and xarray for processing weather and climate data.

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.
Google-Earth-Engine-Python-Examples
csaybar/EEwPython

Tutorials

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

Packages

Earth Engine QGIS Plugin (Website, GitHub) - Integrates Google Earth Engine and QGIS using Python API.

Repositories

qgis-earthengine-examples - A collection of 300+ Python examples for using Google Earth Engine in QGIS.

Tutorials

Creating Maps with Google Earth Engine

GitHub Developers

Community

Individuals

Twitter

Bots

Google affiliated

Individuals

Apps

Earth Engine Apps - Google
An image gallery of almost all publicly available Google Earth Engine Apps - Philipp Gärtner
A searchable list of all publicly available Google Earth Engine Apps

Free Courses

Presentations

geemap

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

General

SERVIR Global - Introduction to Google Earth Engine

Videos

Google

General

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

geemap

geemap tutorials on YouTube
geemap tutorials on 哔哩哔哩
geemap tutorials on 西瓜视频
GeoInsider webinar - Cloud computing and interactive mapping with Earth Engine and open-source GIS (video - slides)
GeoInsider webinar 2 - Using Google Earth Engine for large-scale geospatial analysis: A case study of automated surface water mapping (video | slides)

Projects

Google Earth Engine on Research Gate

Websites

Datasets

Community Datasets

awesome-gee-community-datasets

Landsat

Sentinel

NAIP

NAIP: National Agriculture Imagery Program

Land Cover

NLCD: USGS National Land Cover Database

Papers

Highlights

Aybar, C., Wu, Q., Bautista, L., Yali, R., & Barja, A. (2020). rgee: An R package for interacting with Google Earth Engine. The Journal of Open Source Software. 5(51), 2272. https://doi.org/10.21105/joss.02272
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
Wu, Q. (2020). geemap: A Python package for interactive mapping with Google Earth Engine. The Journal of Open Source Software. 5(51), 2305. https://doi.org/10.21105/joss.02305

Journal Special Issues

Journal of Remote Sensing, Remote Sensing for Environmental and Societal Changes Using Google Earth Engine (Call for Papers)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Cloud Computing in Google Earth Engine for Remote Sensing (Call for Papers)
Remote Sensing, Google Earth Engine and Cloud Computing Platforms: Methods and Applications in Big Geo Data Science (Call for Papers, Published Papers)
Remote Sensning, Google Earth Engine Applications (Call for Papers, Published Papers)
Remote Sensing of Environment, Remote Sensing of Land Change Science with Google Earth Engine (Call for Papers, Published Papers)

Review

Amani, M., Ghorbanian, A., Ahmadi, A., Kakooei, M., ..., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2020.3021052
Boothroyd, R., Williams, R., Hoey, T., Barrett, B., & Prasojo, O. (2020). Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change. WIREs Water. https://doi.org/10.1002/wat2.1496
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
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
Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2020.112002

Hydrology

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
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
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
Yamazaki, D., Trigg, M.A., 2016. Hydrology: The dynamics of Earth’s surface water. Nature. https://doi.org/10.1038/nature21100

Urban

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
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., Wang, S., 2018. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sens. Environ. 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055
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
Patel, N.N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F.R., Tatem, A.J., Trianni, G., 2015. Multitemporal settlement and population mapping from Landsat using Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 35, 199–208. https://doi.org/10.1016/j.jag.2014.09.005
Weiss, D.J., Nelson, A., Gibson, H.S., Temperley, W., Peedell, S., Lieber, A., Hancher, M., Poyart, E., Belchior, S., Fullman, N., Mappin, B., Dalrymple, U., Rozier, J., Lucas, T.C.D., Howes, R.E., Tusting, L.S., Kang, S.Y., Cameron, E., Bisanzio, D., Battle, K.E., Bhatt, S., Gething, P.W., 2018. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336. https://doi.org/10.1038/nature25181

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
Robinson, N.P., Allred, B.W., Jones, M.O., Moreno, A., Kimball, J.S., Naugle, D.E., Erickson, T.A., Richardson, A.D., 2017. A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sensing 9, 863. https://doi.org/10.3390/rs9080863
Xie, Z., Phinn, S.R., Game, E.T., Pannell, D.J., Hobbs, R.J., Briggs, P.R., McDonald-Madden, E., 2019. Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation. Remote Sens. Environ. 232, 111317. https://doi.org/10.1016/j.rse.2019.111317

Agriculture

Dong, J., Xiao, X., Menarguez, M.A., Zhang, G., Qin, Y., Thau, D., Biradar, C., Moore, B., 3rd, 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sens. Environ. 185, 142–154. https://doi.org/10.1016/j.rse.2016.02.016
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
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

Wetlands

Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., Montgomery, J., Hopkinson, C., 2019. Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results. Remote Sensing 11, 842. https://doi.org/10.3390/rs11070842
Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., Wu, Z., Sun, R., Lan, G., Xie, G., Clinton, N., Giri, C., 2017. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS J. Photogramm. Remote Sens. 131, 104–120. https://doi.org/10.1016/j.isprsjprs.2017.07.011
Hird, J.N., DeLancey, E.R., McDermid, G.J., Kariyeva, J., 2017. Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sensing 9, 1315. https://doi.org/10.3390/rs9121315
Mahdianpari, M., Brisco, B., Granger, J. E., Mohammadimanesh, F., Salehi, B., Banks, S., ... & Weng, Q. (2020). The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine. Canadian Journal of Remote Sensing, 46(3), 360-375. https://doi.org/10.1080/07038992.2020.1802584
Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., Gill, E., 2018. The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sensing 11, 43. https://doi.org/10.3390/rs11010043
Wang, X., Xiao, X., Zou, Z., Chen, B., Ma, J., Dong, J., Doughty, R.B., Zhong, Q., Qin, Y., Dai, S., Li, X., Zhao, B., Li, B., 2020. Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine. Remote Sens. Environ. 238, 110987. https://doi.org/10.1016/j.rse.2018.11.030
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
Yancho, J. M. M., Jones, T. G., Gandhi, S. R., Ferster, C., Lin, A., & Glass, L. (2020). The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sensing, 12(22), 3758. https://doi.org/10.3390/rs12223758

Land Cover

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
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G., 2013. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693
Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., Zhu, Z., 2017. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. 202, 166–176. https://doi.org/10.1016/j.rse.2017.02.021
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

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
Tellman, B., Sullivan, J.A., Kuhn, C., Kettner, A.J., Doyle, C.S., Brakenridge, G.R., Erickson, T.A., Slayback, D.A., 2021. Satellite imaging reveals increased proportion of population exposed to floods. Nature 596, 80–86. https://doi.org/10.1038/s41586-021-03695-w

Coastal

Vos, K., Splinter, K.D., Harley, M.D., Simmons, J.A., Turner, I.L., 2019. CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery Environmental Modelling and Software. 122, 104528. https://doi.org/10.1016/j.envsoft.2019.104528

原文：https://github.com/giswqs/Awesome-GEE

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Table of Contents

Earth Engine official websites

Get Started

Get Help

JavaScript API

Playground

Repositories

Tutorials

Python API

Installation

Packages

Repositories

Tutorials

R

Packages

Repositories

Tutorials

QGIS

Packages

Repositories

Tutorials

GitHub Developers

Community

Individuals

Twitter

Bots

Google affiliated

Individuals

Apps

Free Courses

Presentations

geemap

General

Videos

Google

General

geemap

Projects

Websites

Datasets

Community Datasets

Landsat

Sentinel

NAIP

Land Cover

Papers

Highlights

Journal Special Issues

Review

Hydrology

Urban

Vegetation

Agriculture

Wetlands

Land Cover

Disaster Management

Coastal

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