Awesome-GEE
Awesome Earth Engine 
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
- Get Help
- JavaScript API
- Python API
- R
- QGIS
- GitHub Developers
- Apps
- Free Courses
- Presentations
- Videos
- Projects
- Websites
- Datasets
- Papers
- Contributing
- License
Earth Engine official websites
- Official homepage
- JavaScript Code Editor
- API Documentation
- Data Catalog
- Timelapse
- Earth Engine Apps
- Blog
- Sign up
- Developer Forum
- Issue Tracker
- Earth Engine API on GitHub
- Google Earth Engine Community Tutorials
- Google Earth Engine Community Developer Resources
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
- Earth Engine Developer Forum
- GIS Stack Exchange
- Report a bug
- Dataset requests
- Feature requests
- Slack channel for geemap and Earth Engine
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.
- spectral - Awesome Spectral Indices for the Google Earth Engine JavaScript API (Code Editor).
- msslib - An Earth Engine JavaScript library for working with Landsat MSS image data.
- geeSharp - Pan-sharpening in the Earth Engine Code Editor.
- snazzy - Custom basemap styles in the Earth Engine Code Editor.
- ee-polyfill - Modern Javascript methods (ES6+) for the Earth Engine Code Editor.
- gee-blend - Various blending functions for Google Earth Engine.
- OpenEarthEngineLibrary - Collection of code goodies for Google Earth Engine (GEE).
Tutorials
- Introduction to Google Earth Engine
- Introduction to JavaScript for Earth Engine
- Introduction to the Earth Engine JavaScript API
- Global Forest Change Analysis
- Global Surface Water Change Analysis
- Beginner’s Cookbook
- Combining FeatureCollections
- Customizing Base Map Styles
- Forest Cover and Loss Estimation
- Getting Started with Drawing Tools
- Identifying Annual First Day of No Snow Cover
- Interactive Region Reduction App
- Land Surface Temperature in Uganda
- Landsat ETM+ to OLI Harmonization
- MODIS NDVI Times Series Animation
- Non-parametric trend analysis
- GEE 开发 on 知乎 by 无形的风
- Calculating Area in Google Earth Engine
- Extracting Time Series using Google Earth Engine
- Histogram Matching in Google Earth Engine
- Getting Git Right on Google Earth Engine
- AmericaView - Google Earth Engine (GEE) tutorials
- Earth Lab - Introduction to the Google Earth Engine code editor
- Coding Club - Intro to the Google Earth Engine
- Global Snow Observatory - Google Earth Engine Tutorials
- GEARS - Getting started with Google Earth Engine
- An Introduction to Remote Sensing for Ecologists Using Google Earth Engine
- An introduction to Google Earth Engine
Books
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.
- RadGEEToolbox - Python package simplifying large-scale operations using Google Earth Engine (GEE) Python API for users who utilize Landsat (5, 8, & 9) and Sentinel 1 & 2 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.
- taskee - Monitor your Earth Engine tasks and get notifications on your phone or computer.
- geedim - Search, composite, and download Earth Engine imagery, without size limits.
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
- geemap and Earth Engine Python API tutorials
- A Quick Introduction to Google Earth Engine
- Google Earth Engine (GEE) and Image Analysis
- Earth Engine Python API Colab Setup
- Earth Engine TensorFlow demonstration notebook
- Earth Lab - Calculating the area of polygons in Google Earth Engine
- Semantic Segmentation of GEE High Resolution Imagery
Books
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
- rgee tutorial #1: Creating global land surface temperature maps
- rgee tutorial #2: Satellite image processing
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
GitHub Developers
Community
Individuals
- Cesar Aybar
- Justin Braaten
- Tirthankar “TC” Chakraborty
- Diego Garcia Diaz
- Gennadii Donchyts
- Ujaval Gandhi
- Philipp Gärtner
- Eduardo Lacerda
- Kel Markert
- Mort Canty
- Keiko Nomura
- Rodrigo E. Principe
- Mark Radwin
- Samapriya Roy
- Sabrina Szeto
- Qiusheng Wu
Bots
Google affiliated
Individuals
- Cesar Aybar
- Justin Braaten
- Tirthankar “TC” Chakraborty
- Morgan Crowley
- Diego Garcia Diaz
- Gennadii Donchyts
- Ujaval Gandhi
- Philipp Gärtner
- Belize GEO
- Mort Canty
- Kel Markert
- Keiko Nomura
- Samapriya Roy
- Sabrina Szeto
- Dave Thau
- Qiusheng Wu
- Iain H Woodhouse
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
- Earth Engine App Filter by Philipp Gärtner
Free Courses
- End-to-End Google Earth Engine - by Ujaval Gandhi
- Spatial Data Management with Earth Engine - by Qiusheng Wu
- Professor Iain Woodhouse’s guide to GEE resources and 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
Videos
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
- Global Surface Water Explorer
- Global Forest Cover Change
- Global Forest Watch
- Map Of Life
- Climate Engine
- Surface Water Mapping Tool
- Surface water changes (1985-2016)
- Decision Support Tools
- Earth Map
- CoastSat shoreline change database
Datasets
Community Datasets
Landsat
- Landsat 9 Surface Reflectance
- Landsat 9 TOA Reflectance
- Landsat 8 Surface Reflectance
- Landsat 8 TOA Reflectance
Sentinel
NAIP
Land Cover
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
- Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. (2022). Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review. Remote Sensing, 14(14), 3253. https://doi.org/10.3390/rs14143253
- Yang, L., Driscol, J., Sarigai, S., Wu, Q., Lippitt, C. D., & Morgan, M. (2022). Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing. Sensors, 22(6), 2416. https://doi.org/10.3390/s22062416
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
- Radwin, M., & Bowen, B. (2024). Evolution of Great Salt Lake’s Exposed Lakebed (1984-2023): Variations in Sediment Composition, Water, and Vegetation from Landsat OLI and Sentinel MSI Satellite Reflectance Data. Geosites, 51, 1–23. https://doi.org/10.31711/ugap.v51i.134
- 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
- Radwin, M., & Bowen, B. (2024). Evolution of Great Salt Lake’s Exposed Lakebed (1984-2023): Variations in Sediment Composition, Water, and Vegetation from Landsat OLI and Sentinel MSI Satellite Reflectance Data. Geosites, 51, 1–23. https://doi.org/10.31711/ugap.v51i.134
- 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
- Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., … & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 1-17. https://doi.org/10.1038/s41597-022-01307-4
- 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
- Radwin, M., & Bowen, B. (2024). Evolution of Great Salt Lake’s Exposed Lakebed (1984-2023): Variations in Sediment Composition, Water, and Vegetation from Landsat OLI and Sentinel MSI Satellite Reflectance Data. Geosites, 51, 1–23. https://doi.org/10.31711/ugap.v51i.134
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
Contributing
Contributions welcome! Read the contribution guidelines first.
License
To the extent possible under law, Qiusheng Wu has waived all copyright and related or neighboring rights to this work.