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| geemap/geemap_v*.py | ||
| .Python | ||
| env/ | ||
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| @ARTICLE{Gorelick2017, | ||
| title = "{Google Earth Engine: Planetary-scale geospatial analysis for | ||
| everyone}", | ||
| author = "Gorelick, Noel and Hancher, Matt and Dixon, Mike and | ||
| Ilyushchenko, Simon and Thau, David and Moore, Rebecca", | ||
| journal = "Remote sensing of environment", | ||
| publisher = "Elsevier", | ||
| volume = 202, | ||
| pages = "18--27", | ||
| month = "1~" # dec, | ||
| year = 2017, | ||
| url = "http://www.sciencedirect.com/science/article/pii/S0034425717302900", | ||
| keywords = "Cloud computing; Big data; Analysis; Platform; Data | ||
| democratization; Earth Engine", | ||
| issn = "0034-4257", | ||
| doi = "10.1016/j.rse.2017.06.031" | ||
| } | ||
|
|
||
| @ARTICLE{Hansen2013, | ||
| title = "{High-resolution global maps of 21st-century forest cover change}", | ||
| author = "Hansen, M C and Potapov, P V and Moore, R and Hancher, M and | ||
| Turubanova, S A and Tyukavina, A and Thau, D and Stehman, S V | ||
| and Goetz, S J and Loveland, T R and Kommareddy, A and Egorov, A | ||
| and Chini, L and Justice, C O and Townshend, J R G", | ||
| journal = "Science", | ||
| publisher = "science.sciencemag.org", | ||
| volume = 342, | ||
| number = 6160, | ||
| pages = "850--853", | ||
| month = "15~" # nov, | ||
| year = 2013, | ||
| url = "http://dx.doi.org/10.1126/science.1244693", | ||
| language = "en", | ||
| issn = "0036-8075, 1095-9203", | ||
| pmid = "24233722", | ||
| doi = "10.1126/science.1244693" | ||
| } | ||
|
|
||
| @ARTICLE{Hird2017, | ||
| title = "{Google Earth Engine, {Open-Access} Satellite Data, and Machine | ||
| Learning in Support of {Large-Area} Probabilistic Wetland | ||
| Mapping}", | ||
| author = "Hird, Jennifer N and DeLancey, Evan R and McDermid, Gregory J | ||
| and Kariyeva, Jahan", | ||
| publisher = "Multidisciplinary Digital Publishing Institute", | ||
| volume = 9, | ||
| number = 12, | ||
| pages = "1315", | ||
| month = "14~" # dec, | ||
| year = 2017, | ||
| url = "http://www.mdpi.com/2072-4292/9/12/1315", | ||
| keywords = "VIP;Earth Engine", | ||
| language = "en", | ||
| doi = "10.3390/rs9121315" | ||
| } | ||
|
|
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| @ARTICLE{Kennedy2018, | ||
| title = "{Implementation of the LandTrendr Algorithm on Google Earth | ||
| Engine}", | ||
| author = "Kennedy, Robert E and Yang, Zhiqiang and Gorelick, Noel and | ||
| Braaten, Justin and Cavalcante, Lucas and Cohen, Warren B and | ||
| Healey, Sean", | ||
| journal = "Remote Sensing", | ||
| publisher = "Multidisciplinary Digital Publishing Institute", | ||
| volume = 10, | ||
| number = 5, | ||
| pages = "691", | ||
| month = "1~" # may, | ||
| year = 2018, | ||
| url = "http://www.mdpi.com/2072-4292/10/5/691", | ||
| language = "en", | ||
| doi = "10.3390/rs10050691" | ||
| } | ||
|
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| @ARTICLE{Li2019, | ||
| title = "{A dataset of 30 m annual vegetation phenology indicators | ||
| (1985--2015) in urban areas of the conterminous United States}", | ||
| author = "Li, Xuecao and Zhou, Yuyu and Meng, Lin and Asrar, Ghassem R and | ||
| Lu, Chaoqun and Wu, Qiusheng", | ||
| journal = "Earth System Science Data", | ||
| publisher = "earth-syst-sci-data.net", | ||
| year = 2019, | ||
| url = "https://www.earth-syst-sci-data.net/11/881/2019/essd-11-881-2019.html", | ||
| } | ||
|
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| @ARTICLE{Liu2020, | ||
| title = "{High-spatiotemporal-resolution mapping of global urban change | ||
| from 1985 to 2015}", | ||
| author = "Liu, Xiaoping and Huang, Yinghuai and Xu, Xiaocong and Li, | ||
| Xuecao and Li, Xia and Ciais, Philippe and Lin, Peirong and | ||
| Gong, Kai and Ziegler, Alan D and Chen, Anping and Gong, Peng | ||
| and Chen, Jun and Hu, Guohua and Chen, Yimin and Wang, Shaojian | ||
| and Wu, Qiusheng and Huang, Kangning and Estes, Lyndon and Zeng, | ||
| Zhenzhong", | ||
| journal = "Nature Sustainability", | ||
| publisher = "Nature Publishing Group", | ||
| pages = "1--7", | ||
| month = "4~" # may, | ||
| year = 2020, | ||
| url = "https://www.nature.com/articles/s41893-020-0521-x", | ||
| language = "en", | ||
| issn = "2398-9629, 2398-9629", | ||
| doi = "10.1038/s41893-020-0521-x" | ||
| } | ||
|
|
||
| @ARTICLE{Pekel2016, | ||
| title = "{High-resolution mapping of global surface water and its long-term | ||
| changes}", | ||
| author = "Pekel, Jean-Fran{\c c}ois and Cottam, Andrew and Gorelick, Noel | ||
| and Belward, Alan S", | ||
| journal = "Nature", | ||
| volume = 540, | ||
| number = 7633, | ||
| pages = "418--422", | ||
| month = "15~" # dec, | ||
| year = 2016, | ||
| url = "http://dx.doi.org/10.1038/nature20584", | ||
| language = "en", | ||
| issn = "0028-0836, 1476-4687", | ||
| pmid = "27926733", | ||
| doi = "10.1038/nature20584" | ||
| } | ||
|
|
||
| @ARTICLE{Wu2019, | ||
| title = "{Integrating LiDAR data and multi-temporal aerial imagery to map | ||
| wetland inundation dynamics using Google Earth Engine}", | ||
| author = "Wu, Qiusheng and Lane, Charles R and Li, Xuecao and Zhao, | ||
| Kaiguang and Zhou, Yuyu and Clinton, Nicholas and DeVries, Ben | ||
| and Golden, Heather E and Lang, Megan W", | ||
| journal = "Remote sensing of environment", | ||
| publisher = "Elsevier", | ||
| volume = 228, | ||
| pages = "1--13", | ||
| month = "1~" # jul, | ||
| year = 2019, | ||
| url = "http://www.sciencedirect.com/science/article/pii/S0034425719301609", | ||
| keywords = "Wetland hydrology; Inundation; Topographic depressions; Surface | ||
| water; LiDAR; Google Earth Engine", | ||
| issn = "0034-4257", | ||
| doi = "10.1016/j.rse.2019.04.015" | ||
| } | ||
|
|
||
| @ARTICLE{QuantStack2019, | ||
| title = "{Interactive GIS in Jupyter with ipyleaflet - Jupyter Blog}", | ||
| author = "{QuantStack}", | ||
| publisher = "Jupyter Blog", | ||
| month = "24~" # sep, | ||
| year = 2019, | ||
| url = "https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a" | ||
| } |
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| --- | ||
| title: "geemap: A Python package for interactive mapping with Google Earth Engine" | ||
| tags: | ||
| - Python | ||
| - Google Earth Engine | ||
| - ipyleaflet | ||
| - mapping | ||
| - Jupyter notebook | ||
| authors: | ||
| - name: Qiusheng Wu | ||
| orcid: 0000-0001-5437-4073 | ||
| affiliation: "1" | ||
| affiliations: | ||
| - name: Department of Geography, University of Tennessee, Knoxville, TN 37996, United States | ||
| index: 1 | ||
| date: 21 May 2020 | ||
| bibliography: paper.bib | ||
| --- | ||
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| # Summary | ||
|
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| **geemap** is a Python package for interactive mapping with [Google Earth Engine](https://earthengine.google.com/) (GEE), | ||
| which is a cloud computing platform with a [multi-petabyte catalog](https://developers.google.com/earth-engine/datasets/) | ||
| of satellite imagery and geospatial datasets (e.g., Landsat, Sentinel, MODIS, NAIP) [@Gorelick2017]. During the past few years, | ||
| GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, | ||
| and global scales. Some of the notable environmental applications include mapping global forest change [@Hansen2013], | ||
| global urban change [@Liu2020], global surface water change [@Pekel2016], wetland inundation dynamics [@Wu2019], vegetation | ||
| phenology [@Li2019], and time series analysis [@Kennedy2018]. | ||
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| GEE provides both JavaScript and Python APIs for making computational requests to the Earth Engine servers. | ||
| Compared with the comprehensive [documentation](https://earthengine.google.com/) and interactive IDE | ||
| (i.e., [GEE JavaScript Code Editor](https://code.earthengine.google.com/)) of the GEE JavaScript API, the | ||
| GEE Python API lacks good documentation and functionality for visualizing results interactively. | ||
| The **geemap** Python package is created to fill this gap[. It is built upon | ||
| [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [ipywidgets](https://github.com/jupyter-widgets/ipywidgets), | ||
| enabling GEE users to analyze and visualize Earth Engine datasets interactively with Jupyter notebooks. | ||
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| # geemap Audience | ||
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| **geemap** is intended for students and researchers, who would like to utilize the Python ecosystem of diverse libraries and | ||
| tools to explore Google Earth Engine. It is also designed for existing GEE users who would like to transition from the GEE | ||
| JavaScript API to Python API. The automated JavaScript-to-Python [conversion module](https://github.com/giswqs/geemap/blob/master/geemap/conversion.py) | ||
| of the **geemap** package can greatly reduce the time needed to convert existing GEE JavaScripts to Python scripts and Jupyter notebooks. | ||
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| # geemap Functionality | ||
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| The interactive mapping functionality of the **geemap** package is built upon [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium), both of which rely on Jupyter notebooks for creating interactive maps. A key difference between ipyleaflet and folium is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only [@QuantStack2019]. It should be noted that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet. Therefore, if one wants to use **geemap** on Google Colab, one should use `import geemap.eefolium as geemap`, which provides limited interactive mapping functionality. To utilize the full interactive mapping functionality of **geemap**, one should use `import geemap` on a local computer or secured server with Jupyter notebook installed. | ||
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| The key functionality of **geemap** is organized into several modules: | ||
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| - [geemap](https://geemap.readthedocs.io/en/latest/source/geemap.html#module-geemap.geemap): the main module for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets. | ||
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| - [eefolium](https://geemap.readthedocs.io/en/latest/source/geemap.html#module-geemap.eefolium): a module for interactive mapping with Earth Engine and folium. It is designed for users to run geemap with Google Colab. | ||
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| - [conversion](https://geemap.readthedocs.io/en/latest/source/geemap.html#module-geemap.conversion): utilities for automatically converting Earth Engine JavaScripts to Python scripts and Jupyter notebooks. | ||
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| - [basemaps](https://geemap.readthedocs.io/en/latest/source/geemap.html#module-geemap.basemaps): a module for adding various XYZ and WMS tiled basemaps. | ||
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| - [legends](https://geemap.readthedocs.io/en/latest/source/geemap.html#module-geemap.legends): a module for adding customized legends to interactive maps. | ||
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| # geemap Tutorials | ||
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| Various tutorials and documentation are available for using **geemap**, including: | ||
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| - [20+ video tutorials with corresponding notebook examples](https://github.com/giswqs/geemap/tree/master/examples) | ||
| - [360+ Jupyter notebook examples using Google Earth Engine](https://github.com/giswqs/earthengine-py-notebooks) | ||
| - [Complete documentation on geemap modules and methods](https://geemap.readthedocs.io/en/latest/source/geemap.html) | ||
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| # Acknowledgements | ||
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| The author would like to thank the developers of ipyleaflet and ipywidgets, which empowers the interactive mapping functionality of **geemap**, including [martin Renou](https://github.com/martinRenou), [Sylvain Corlay](https://github.com/SylvainCorlay), and [David Brochart](https://github.com/davidbrochart). The author would also like to acknowledge source code contributions from [Justin Braaten](https://github.com/jdbcode), [Cesar Aybar](https://github.com/csaybar), [Oliver Burdekin](https://github.com/Ojaybee), [Diego Garcia Diaz](https://github.com/Digdgeo), and [Stephan Büttig](https://twitter.com/stephan_buettig). | ||
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| # References |