Introduction to GEE

Introduction to GEE#

Background#

So far in this course, we have covered the use of python in handling geodata. Specifically, we have learned how to manage (large) raster and vector data and we got in contact with a large variety of tools and techniques. What all these had in common, was that usually you were provided with some kind of data, prepared by us. In addition, so far there was always enough computing power and disc space available for you. However, in your own research or in work you do outside of the university you will likely face the issue that you want to work with large data, but (a) won’t have the infrastructure to process them, and (b) may face restrictions because you want to access data as a private person. This is, why we want to expose you during the next few weeks to Google Earth Engine (GEE) as a huge data and processing engine, that you can use for your own work. GEE is around since ~2012 and it received a lot of attention with the publication by Hansen et al. (2013) in Science. Since then, the number of studies using GEE has exploded and the data catalogue seems to be growing day after day.

There are a lot of different tutorials and packages that help you doing some sort of analysis. Many of them use the language JavaScript , which GEE was initially prepared for, but increasingly you find tutorials and tools that use Python as an API. What these tools and tutorials all have in common is that they help you running some analysis in GEE and visualize the results. This can include time series analyses, image classifications, or simply the visualization of datasets (which there are a lot in GEE). They are designed to visualize the results directly in the browser, and, as we will learn, compute the results on-the-fly when zooming in/out or moving to a different place on the Earth. This is all pretty fantastic, and - as already said - there are a lot of tutorials out there, many of which we will provide links to. Though, GEE also has a number of limitations, such as a limited amount of algorithms implemented that can be applied. While for example in case of “simple” classifications this is not too big of a problem, in our daily research we often want to do more, for example designing new methods and tools that use image classifications or regressions as input. In the majority of these cases, GEE does not have the capabilities to flexibly implement and test new analysis methods. Thus, at some point in our analysis we need to export large amounts of pre-processed data from GEE to our own drives and work on them further. Here is, where it becomes tricky as GEE has mechnisms in its architecture that prevents exports of large data at once.

This part of the course therefore will not provide yet another tutorial of doing image classifications in GEE, but we want to continue with the general research line of large-scale processing. Specifically, we want to teach you in these sessions on how to use GEE as a large pre-processing-engine from which you export intermediate results to your own computer. Focussing on this part, will in our eyes nicely complement existing tutorials, which we will provide links to.

../../_images/gee_overview.png — Fig. 4 Overview about the different components, that *GEE* offers. The figure is derived from the *GEE*-website).#

We will focus on the first two elements (from the left) and we will use python as our API. In short, what GEE provides to us, is:

An extensive archive of data, including many complete satellite image archives (e.g., Landsat, Sentinel-1/2, MODIS, Planet-NICFI mosaics) as well as land-cover products (e.g., global forest watch, ESA World-Cover) both at the global and national (primarily United States) scale.
Simple (e.g., linear regression, random forests) & more advanced processing algorithms, particularly in the satellite image domain (e.g., LandTrendr, CCDC)
Nearly unlimited computing power

On the opposite, what it does not provide to us:

advanced processing solutions, particularly with respect to remote sensing data processing (e.g., FORCE); but also a higher diversity of classification and rgression algorithm (e.g., gradient boosting regression)
Although GEE provides some detail on how it handles data and applies algorithms, for many instances it remains a “black box”.
Nice cartography (although this is getting better)
DIY-parallelization (“The price of liberation from these details is that the user is unable to influence them.” (Gorelick, et al. 2017))
Easy extrapolation across large geographic spaces and export/download of these processing results

Having these elements in mind, what we want to do in this course, is:

to advertise GEE as a great source to do large area processing (but not necessarily only)
Show its power, but also its limitations and develop ways to work around this
Motivate you to use the best of the two worlds: (1) Data archives and processing power in GEE, (2) Advanced functionality of local python processing and use of gdal and osr

There are many important aspects with regards to GEE that you will probably learn if you keep using it. Though, one fundamental principle is important for our understanding: the distinction between server- and client-side operations. In general, we have a client side library that will be translated into complex geospatial analyses into EE requests:

../../_images/server_client.png — Fig. 5 Schematic representation about the differentiation between server-side and client-side operations (Source: https://geohackweek.github.io/GoogleEarthEngine/).#

This is an important aspect, as we will have to always think about what a certain operation is done locally on your computer, and what operation we want GEE to do. In other words, on the client-side (i.e., our computers) we create and manipulate “proxy objects”, which do not contain any data, but are just handles for objects on the server-side. This is really important to consider, because it means that, compared to the processes we have been thinking so far, we hardly can create/modify variables step-by-step.

Client library#

The client library lets you run processing tasks on the Google servers. The library can be accessed via a JavaScript and Python API (Application Interface). To better understand the API, check out the documentation. Below is a list of some examples.

Client Libraries:

ee.Algorithms
ee.Array
ee.Blob
ee.Classifier
ee.Image
ee.ImageCollection
ee.Reducer

Some libraries such as ee.Algorithms and ee.Reducer contain functions that are applied to other EE objects. They work like a collection of tools. You can access them as follows:

ee.Algorithms.HillShadow(image, azimuth, zenith, neighborhoodSize, hysteresis)

Other libraries correspond to EE data types that have their own functions (methods), e.g., ee.Dictionary, ee.Image.

Data types#

There are many good GEE tutorials online, including this one. We do not want to re-invent those tutorials and possibly create a terrible new version of it. Instead, we want to focus on doing these types of analyses and extract the data to our own drive - both for individual locations but also across large geographic extents. Here, we briefly introduce the basic elements of geographic features that we will work with in the context of the course. For a more complete overview of Earth Engine’s data types see here. When we introduce the data types, we will also use some visualization tools, specifically from the geemap library. This package has become quite popular in the last years. The author/developer is Quisheng Wu, a professor at the University of Tenessee. Originally being a thematic expert in Wetland mapping, he has become an important developer for GEE using python. The strength of his tool is that it brings together the functionality of QGIS in a ipynb. Many tools and tricks for visualization are available through his package, and we really recommend checking it out. For example, the package allows to download TimeLapse videos (see some examples for what a TimeLaps is here) and also some image composites and image classifications. The package also builds on different visualization backends, all of which have a different functionality. You can find out more about this here. The weakness of the tool/library is that it is not perfect if we want to extract large amounts of data from GEE or do complex operations as the package of course also faces memory and computation restrictions from the side of GEE. So, for our course, it is mostly suitable for visualizing data. The rest - as you have learned during the past weeks of the course - will have to be implemented manually.

We start by importing the package and by authenticating our computer with google. This basically creates a connection between you and your computer and stores the connection details in something similar to a cookie. This is done by the code line ee.Authenticate() and has only be execute once. What you need to do, however, every time you start working is to instantiate a GEE session via ee.Initialize(). Below is a good code chunk that you can possibly use as a standard chunk inside your scripts.

import ee
import geemap.foliumap as geemap
try:
    ee.Initialize()
except Exception as e:
    ee.Authenticate()
    ee.Initialize()

Vector data#

Contrary to the previous parts of this book, we will start with introducing vector data, primarily because we will use them to make some spatial selections later on with raster data. In GEE vector data are considered as features. A feature consists of a Geometry and a dictionary of properties (comparable to attributes). Below you find an example for how to build geometries and correspoinding features:

point_geom = ee.Geometry.Point([-60, -20])
rectangle_geom = ee.Geometry.Rectangle([-60, -20, -62, -22])
polygon_geom = ee.Geometry.Polygon([[[-60, -20], [-62, -20], [-62, -22], [-60, -22], [-60, -20]]])

point_feat = ee.Feature(point_geom, {'ID': 1})
rectangle_feat = ee.Feature(rectangle_geom, {'ID': 2})
polygon_feat = ee.Feature(polygon_geom, {'ID': 3})

Many features combined result in a ee.FeatureCollection(), which corresponds to what we have got to know as a layer in general vector data. An ee.FeatureCollection() is build through a list of features. While it is in principle possible to combined different types of features/geometries (e.g., points and polygons), we generally recommend to keep these separate for data sanity. Keep in mind, that these are only container objects, which are only created server-side. How we retrieve these objects, we show in the next chapter.

Besides manually created feature collections, there are many different datasets stored in GEE. What we have learned in our section on vector selections is that vector data can be either selected by location or by attribute. In GEE we do this trough applying a filter to a ee.FeatureCollection(), which corresponds to the .SetAttributeFilter() method we learned before. Below you find an example, that loads the FAO country borders from GEE (i.e., an ee.FeatureCollection())`, and then selects the country Germany. We then use geemap to visualize the feature on a map.

fao = ee.FeatureCollection("FAO/GAUL/2015/level0")
ger = fao.filter(ee.Filter.eq('ADM0_NAME', 'Germany')) # Filter by country name

FeatureCollection FAO/GAUL/2015/level0 (1 element, 9 columns)
- type:FeatureCollection
- id:FAO/GAUL/2015/level0
- version:1701682497889631
- columns: Object (9 properties)
  - ADM0_CODE:Integer
  - ADM0_NAME:String
  - DISP_AREA:String
  - EXP0_YEAR:Integer
  - STATUS:String
  - STR0_YEAR:Integer
  - Shape_Area:Float
  - Shape_Leng:Float
  - system:index:String
- properties: Object (1 property)
  - system:asset_size:139687468
- features: List (1 element)
  - 0: Feature (GeometryCollection, 8 properties)
    - type:Feature
    - id:00000000000000000018
    - geometry: GeometryCollection
      - type:GeometryCollection
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        type:LineString
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        0:6.150380098202273
        1:49.99742088853664
        1805: [6.153269626524685, 49.99355034426062]
        0:6.153269626524685
        1:49.99355034426062
        1806: [6.158259326475018, 49.99107109064168]
        0:6.158259326475018
        1:49.99107109064168
        1807: [6.160042998285401, 49.99082142097731]
        0:6.160042998285401
        1:49.99082142097731
        1808: [6.169259948551333, 49.98954160086704]
        0:6.169259948551333
        1:49.98954160086704
        1809: [6.170829510048288, 49.98843578943001]
        0:6.170829510048288
        1:49.98843578943001
        1810: [6.172479376320466, 49.987271948307715]
        0:6.172479376320466
        1:49.987271948307715
        1811: [6.167351477619378, 49.97512979213094]
        0:6.167351477619378
        1:49.97512979213094
        1812: [6.1673514558287925, 49.97512531524297]
        0:6.1673514558287925
        1:49.97512531524297
        1813: [6.1684216268376195, 49.97311879009958]
        0:6.1684216268376195
        1:49.97311879009958
        1814: [6.170628861585586, 49.96897177616306]
        0:6.170628861585586
        1:49.96897177616306
        1815: [6.180670816949724, 49.959661216511826]
        0:6.180670816949724
        1:49.959661216511826
        1816: [6.183480009741891, 49.95995103461001]
        0:6.183480009741891
        1:49.95995103461001
        1817: [6.196919737292015, 49.96135122220356]
        0:6.196919737292015
        1:49.96135122220356
        1818: [6.1977803348867075, 49.95938914962237]
        0:6.1977803348867075
        1:49.95938914962237
        1819: [6.1991180489466995, 49.95633917748296]
        0:6.1991180489466995
        1:49.95633917748296
        1820: [6.203751084628409, 49.953850966052634]
        0:6.203751084628409
        1:49.953850966052634
        1821: [6.21756987406122, 49.95209855601291]
        0:6.21756987406122
        1:49.95209855601291
        1822: [6.219781529112007, 49.945940511094314]
        0:6.219781529112007
        1:49.945940511094314
        1823: [6.224383348857374, 49.941262932574034]
        0:6.224383348857374
        1:49.941262932574034
        1824: [6.22694732496787, 49.93865878815346]
        0:6.22694732496787
        1:49.93865878815346
        1825: [6.226951837764017, 49.938658781166176]
        0:6.226951837764017
        1:49.938658781166176
        1826: [6.22678682175145, 49.93703570876737]
        0:6.22678682175145
        1:49.93703570876737
        1827: [6.226010957010245, 49.92929025809828]
        0:6.226010957010245
        1:49.92929025809828
        1828: [6.235004905579497, 49.917674285950355]
        0:6.235004905579497
        1:49.917674285950355
        1829: [6.235009384189174, 49.917669806248796]
        0:6.235009384189174
        1:49.917669806248796
        1830: [6.230487906773569, 49.91239917687273]
        0:6.230487906773569
        1:49.91239917687273
        1831: [6.242687973656313, 49.89852244551753]
        0:6.242687973656313
        1:49.89852244551753
        1832: [6.242687940605903, 49.89851797456226]
        0:6.242687940605903
        1:49.89851797456226
        1833: [6.2456354508604015, 49.897688560585976]
        0:6.2456354508604015
        1:49.897688560585976
        1834: [6.249068924275949, 49.8967209645559]
        0:6.249068924275949
        1:49.8967209645559
        1835: [6.254500144808043, 49.893117983768065]
        0:6.254500144808043
        1:49.893117983768065
        1836: [6.257269199916677, 49.891280855638236]
        0:6.257269199916677
        1:49.891280855638236
        1837: [6.260622467574654, 49.886893102053534]
        0:6.260622467574654
        1:49.886893102053534
        1838: [6.263008122600465, 49.883771738911065]
        0:6.263008122600465
        1:49.883771738911065
        1839: [6.2689431510609595, 49.88174283581686]
        0:6.2689431510609595
        1:49.88174283581686
        1840: [6.276849180360259, 49.87904062457715]
        0:6.276849180360259
        1:49.87904062457715
        1841: [6.276978415185941, 49.87904954462924]
        0:6.276978415185941
        1:49.87904954462924
        1842: [6.285669275786637, 49.879771898024146]
        0:6.285669275786637
        1:49.879771898024146
        1843: [6.28938813787741, 49.87558932191263]
        0:6.28938813787741
        1:49.87558932191263
        1844: [6.294761342658427, 49.869538322957496]
        0:6.294761342658427
        1:49.869538322957496
        1845: [6.304870141411472, 49.86911912267947]
        0:6.304870141411472
        1:49.86911912267947
        1846: [6.314359079737466, 49.87212901277769]
        0:6.314359079737466
        1:49.87212901277769
        1847: [6.321159227076146, 49.85798479490021]
        0:6.321159227076146
        1:49.85798479490021
        1848: [6.321355418473093, 49.8575745181398]
        0:6.321355418473093
        1:49.8575745181398
        1849: [6.322688733227447, 49.85480097267605]
        0:6.322688733227447
        1:49.85480097267605
        1850: [6.323723200890163, 49.84972647604916]
        0:6.323723200890163
        1:49.84972647604916
        1851: [6.324178002476341, 49.84750141220096]
        0:6.324178002476341
        1:49.84750141220096
        1852: [6.322461248243988, 49.843381183955]
        0:6.322461248243988
        1:49.843381183955
        1853: [6.322465727466128, 49.84337678392005]
        0:6.322465727466128
        1:49.84337678392005
        1854: [6.326741979683683, 49.83973809667607]
        0:6.326741979683683
        1:49.83973809667607
        1855: [6.330781997543526, 49.8393546470072]
        0:6.330781997543526
        1:49.8393546470072
        1856: [6.335918815932964, 49.83885968298208]
        0:6.335918815932964
        1:49.83885968298208
        1857: [6.335923274753355, 49.838864142941844]
        0:6.335923274753355
        1:49.838864142941844
        1858: [6.348498002293714, 49.85081006149578]
        0:6.348498002293714
        1:49.85081006149578
        1859: [6.367199411709234, 49.85112221824244]
        0:6.367199411709234
        1:49.85112221824244
        1860: [6.387829458991457, 49.83509172654598]
        0:6.387829458991457
        1:49.83509172654598
        1861: [6.40845951238146, 49.81906127717393]
        0:6.40845951238146
        1:49.81906127717393
        1862: [6.413391233889586, 49.818356714407365]
        0:6.413391233889586
        1:49.818356714407365
        1863: [6.413730130362198, 49.8183121487962]
        0:6.413730130362198
        1:49.8183121487962
        1864: [6.425760856922934, 49.81659982880151]
        0:6.425760856922934
        1:49.81659982880151
        1865: [6.436899645848887, 49.814963374664956]
        0:6.436899645848887
        1:49.814963374664956
        1866: [6.442362008334852, 49.81416073518684]
        0:6.442362008334852
        1:49.81416073518684
        1867: [6.446179007578734, 49.813273377413914]
        0:6.446179007578734
        1:49.813273377413914
        1868: [6.450138727475671, 49.81235035608468]
        0:6.450138727475671
        1:49.81235035608468
        1869: [6.456479614615464, 49.81260005845639]
        0:6.456479614615464
        1:49.81260005845639
        1870: [6.469451088613561, 49.82246799310009]
        0:6.469451088613561
        1:49.82246799310009
        1871: [6.475756251056077, 49.81966774044464]
        0:6.475756251056077
        1:49.81966774044464
        1872: [6.477588916616393, 49.818851698514166]
        0:6.477588916616393
        1:49.818851698514166
        1873: [6.477856535639231, 49.81859309269826]
        0:6.477856535639231
        1:49.81859309269826
        1874: [6.481138403371609, 49.815431584392144]
        0:6.481138403371609
        1:49.815431584392144
        1875: [6.492098889300094, 49.81226115935964]
        0:6.492098889300094
        1:49.81226115935964
        1876: [6.503389297634308, 49.81206938884812]
        0:6.503389297634308
        1:49.81206938884812
        1877: [6.508700085302677, 49.81003157206357]
        0:6.508700085302677
        1:49.81003157206357
        1878: [6.510216179005563, 49.80870721498552]
        0:6.510216179005563
        1:49.80870721498552
        1879: [6.513667505680652, 49.80570183425024]
        0:6.513667505680652
        1:49.80570183425024
        1880: [6.513672021374703, 49.805701815194844]
        0:6.513672021374703
        1:49.805701815194844
        1881: [6.518559132557503, 49.81189100881043]
        0:6.518559132557503
        1:49.81189100881043
        1882: [6.519013952815197, 49.812051546993786]
        0:6.519013952815197
        1:49.812051546993786
        1883: [6.527000290005549, 49.814891980928664]
        0:6.527000290005549
        1:49.814891980928664
        1884: [6.528458381241435, 49.808029482292675]
        0:6.528458381241435
        1:49.808029482292675
        1885: [6.527669096217308, 49.806682812074214]
        0:6.527669096217308
        1:49.806682812074214
        1886: [6.524280196387995, 49.80093056456772]
        0:6.524280196387995
        1:49.80093056456772
        1887: [6.515027573281106, 49.79401449176133]
        0:6.515027573281106
        1:49.79401449176133
        1888: [6.514461258661191, 49.793590880549644]
        0:6.514461258661191
        1:49.793590880549644
        1889: [6.507085939072653, 49.790821810088474]
        0:6.507085939072653
        1:49.790821810088474
        1890: [6.511192757806644, 49.78788765903566]
        0:6.511192757806644
        1:49.78788765903566
        1891: [6.516650647948884, 49.78399936989392]
        0:6.516650647948884
        1:49.78399936989392
        1892: [6.5131904496789215, 49.77553149917837]
        0:6.5131904496789215
        1:49.77553149917837
        1893: [6.517948285668596, 49.7688785258848]
        0:6.517948285668596
        1:49.7688785258848
        1894: [6.521020611135174, 49.76457998193462]
        0:6.521020611135174
        1:49.76457998193462
        1895: [6.518581486282293, 49.761378358089004]
        0:6.518581486282293
        1:49.761378358089004
        1896: [6.5185769764863135, 49.76137837724565]
        0:6.5185769764863135
        1:49.76137837724565
        1897: [6.506278796082263, 49.756540209645046]
        0:6.506278796082263
        1:49.756540209645046
        1898: [6.505476179434866, 49.755438849760786]
        0:6.505476179434866
        1:49.755438849760786
        1899: [6.5021006116033515, 49.75081023162738]
        0:6.5021006116033515
        1:49.75081023162738
        1900: [6.5019623616642725, 49.74798768201287]
        0:6.5019623616642725
        1:49.74798768201287
        1901: [6.501788488799966, 49.74441143899009]
        0:6.501788488799966
        1:49.74441143899009
        1902: [6.503054882315006, 49.74164685371127]
        0:6.503054882315006
        1:49.74164685371127
        1903: [6.504298967237991, 49.73893122598727]
        0:6.504298967237991
        1:49.73893122598727
        1904: [6.508994352322972, 49.73426698281413]
        0:6.508994352322972
        1:49.73426698281413
        1905: [6.515299598192289, 49.72800193278622]
        0:6.515299598192289
        1:49.72800193278622
        1906: [6.515700857042, 49.725888353406425]
        0:6.515700857042
        1:49.725888353406425
        1907: [6.516387637753516, 49.72228988930826]
        0:6.516387637753516
        1:49.72228988930826
        1908: [6.516392090709931, 49.72228095116571]
        0:6.516392090709931
        1:49.72228095116571
        1909: [6.503331348021052, 49.72772994010777]
        0:6.503331348021052
        1:49.72772994010777
        1910: [6.49946085649465, 49.727261769781265]
        0:6.49946085649465
        1:49.727261769781265
        1911: [6.496950343973962, 49.724296488529866]
        0:6.496950343973962
        1:49.724296488529866
        1912: [6.495969384597696, 49.72314154996139]
        0:6.495969384597696
        1:49.72314154996139
        1913: [6.501998086384115, 49.716988011495694]
        0:6.501998086384115
        1:49.716988011495694
        1914: [6.499581285702187, 49.711262510364186]
        0:6.499581285702187
        1:49.711262510364186
        1915: [6.499581215072672, 49.71125805423035]
        0:6.499581215072672
        1:49.71125805423035
        1916: [6.491073219511389, 49.705010806817164]
        0:6.491073219511389
        1:49.705010806817164
        1917: [6.490841429154525, 49.70484136402425]
        0:6.490841429154525
        1:49.70484136402425
        1918: [6.489450141514267, 49.70394960611129]
        0:6.489450141514267
        1:49.70394960611129
        1919: [6.476139730885142, 49.69541928653845]
        0:6.476139730885142
        1:49.69541928653845
        1920: [6.474405156372932, 49.69495553917106]
        0:6.474405156372932
        1:49.69495553917106
        1921: [6.466659704861586, 49.69288211392434]
        0:6.466659704861586
        1:49.69288211392434
        1922: [6.454134056308203, 49.682858013748564]
        0:6.454134056308203
        1:49.682858013748564
        1923: [6.450589076782418, 49.68002206227678]
        0:6.450589076782418
        1:49.68002206227678
        1924: [6.449193368779182, 49.678644207836285]
        0:6.449193368779182
        1:49.678644207836285
        1925: [6.444310685811755, 49.67382835567622]
        0:6.444310685811755
        1:49.67382835567622
        1926: [6.440101321409493, 49.67290084429753]
        0:6.440101321409493
        1:49.67290084429753
        1927: [6.433492900170678, 49.66782639203796]
        0:6.433492900170678
        1:49.66782639203796
        1928: [6.4275311138488185, 49.6632513383938]
        0:6.4275311138488185
        1:49.6632513383938
        1929: [6.4291987426177695, 49.66175311271793]
        0:6.4291987426177695
        1:49.66175311271793
        1930: [6.431080543519599, 49.66007201970505]
        0:6.431080543519599
        1:49.66007201970505
        1931: [6.436699031712886, 49.660089849491]
        0:6.436699031712886
        1:49.660089849491
        1932: [6.437207329098559, 49.66075425315745]
        0:6.437207329098559
        1:49.66075425315745
        1933: [6.438090181110654, 49.66191806866235]
        0:6.438090181110654
        1:49.66191806866235
        1934: [6.44447122876071, 49.655541574661186]
        0:6.44447122876071
        1:49.655541574661186
        1935: [6.443186967581552, 49.65155956399161]
        0:6.443186967581552
        1:49.65155956399161
        1936: [6.4417377714883735, 49.64707820827607]
        0:6.4417377714883735
        1:49.64707820827607
        1937: [6.4380991481840155, 49.64149983205374]
        0:6.4380991481840155
        1:49.64149983205374
        1938: [6.432030347317009, 49.6321892853049]
        0:6.432030347317009
        1:49.6321892853049
        1939: [6.425841087206284, 49.618000422792846]
        0:6.425841087206284
        1:49.618000422792846
        1940: [6.408999062560388, 49.60703100036274]
        0:6.408999062560388
        1:49.60703100036274
        1941: [6.402132065464266, 49.6000792941538]
        0:6.402132065464266
        1:49.6000792941538
        1942: [6.388469352633414, 49.59660117186571]
        0:6.388469352633414
        1:49.59660117186571
        1943: [6.384884198845269, 49.59454108943057]
        0:6.384884198845269
        1:49.59454108943057
        1944: [6.383238874445954, 49.593600196976496]
        0:6.383238874445954
        1:49.593600196976496
        1945: [6.381067213258559, 49.59024697226127]
        0:6.381067213258559
        1:49.59024697226127
        1946: [6.379091869295691, 49.5871880271453]
        0:6.379091869295691
        1:49.5871880271453
        1947: [6.379096289474254, 49.58718356739358]
        0:6.379096289474254
        1:49.58718356739358
        1948: [6.386190755377415, 49.578992206228925]
        0:6.386190755377415
        1:49.578992206228925
        1949: [6.386190793856899, 49.57898775464451]
        0:6.386190793856899
        1:49.57898775464451
        1950: [6.3823693398028185, 49.57485857911102]
        0:6.3823693398028185
        1:49.57485857911102
        1951: [6.381580090368402, 49.57488537426674]
        0:6.381580090368402
        1:49.57488537426674
        1952: [6.369741091620044, 49.57525989143583]
        0:6.369741091620044
        1:49.57525989143583
        1953: [6.36665095220549, 49.572544315185645]
        0:6.36665095220549
        1:49.572544315185645
        1954: [6.364519507550584, 49.570671528993785]
        0:6.364519507550584
        1:49.570671528993785
        1955: [6.364524020258098, 49.570667065949955]
        0:6.364524020258098
        1:49.570667065949955
        1956: [6.374004041716859, 49.56507976060452]
        0:6.374004041716859
        1:49.56507976060452
        1957: [6.381468569834468, 49.56068314975896]
        0:6.381468569834468
        1:49.56068314975896
        1958: [6.381468591326926, 49.56067862249348]
        0:6.381468591326926
        1:49.56067862249348
        1959: [6.38294901765759, 49.55886824877278]
        0:6.38294901765759
        1:49.55886824877278
        1960: [6.38537024875863, 49.55589852116826]
        0:6.38537024875863
        1:49.55589852116826
        1961: [6.383992381402142, 49.55070364882394]
        0:6.383992381402142
        1:49.55070364882394
        1962: [6.38294003735291, 49.54673952564659]
        0:6.38294003735291
        1:49.54673952564659
        1963: [6.380193238880417, 49.54465261205318]
        0:6.380193238880417
        1:49.54465261205318
        1964: [6.375698474777066, 49.541241443499175]
        0:6.375698474777066
        1:49.541241443499175
        1965: [6.36525972728264, 49.533652057887515]
        0:6.36525972728264
        1:49.533652057887515
        1966: [6.364225272064195, 49.52867571018646]
        0:6.364225272064195
        1:49.52867571018646
        1967: [6.363262086412539, 49.52404266426421]
        0:6.363262086412539
        1:49.52404266426421
        1968: [6.363262033642424, 49.524038222011434]
        0:6.363262033642424
        1:49.524038222011434
        1969: [6.3733396381868745, 49.49826014701586]
        0:6.3733396381868745
        1:49.49826014701586
        1970: [6.373259345095409, 49.498113037926146]
        0:6.373259345095409
        1:49.498113037926146
        1971: [6.370931709811338, 49.493908085910064]
        0:6.370931709811338
        1:49.493908085910064
        1972: [6.371810140726745, 49.49183909050128]
        0:6.371810140726745
        1:49.49183909050128
        1973: [6.373451145512753, 49.48798194096829]
        0:6.373451145512753
        1:49.48798194096829
        1974: [6.371404416418354, 49.47335611273534]
        0:6.371404416418354
        1:49.47335611273534
        1975: [6.370508130190162, 49.46693945638551]
        0:6.370508130190162
        1:49.46693945638551
        1976: [6.374588147118282, 49.45884174343185]
        0:6.374588147118282
        1:49.45884174343185
        1977: [6.386997873684125, 49.46380914933262]
        0:6.386997873684125
        1:49.46380914933262
        1978: [6.396460045152705, 49.462939599001075]
        0:6.396460045152705
        1:49.462939599001075
        1979: [6.405190978188442, 49.46479905853937]
        0:6.405190978188442
        1:49.46479905853937
        1980: [6.412927485045663, 49.46840648900249]
        0:6.412927485045663
        1:49.46840648900249
        1981: [6.422969395513132, 49.47308857292491]
        0:6.422969395513132
        1:49.47308857292491
        1982: [6.429970201230042, 49.47380195589781]
        0:6.429970201230042
        1:49.47380195589781
        1983: [6.439668790392717, 49.4689370960359]
        0:6.439668790392717
        1:49.4689370960359
        1984: [6.445791091052318, 49.46586928620451]
        0:6.445791091052318
        1:49.46586928620451
        1985: [6.462298738553437, 49.46043808043039]
        0:6.462298738553437
        1:49.46043808043039
        1986: [6.46727056958274, 49.45987176797542]
        0:6.46727056958274
        1:49.45987176797542
        1987: [6.475961335198958, 49.45888183059827]
        0:6.475961335198958
        1:49.45888183059827
        1988: [6.48791172556422, 49.451840917966656]
        0:6.48791172556422
        1:49.451840917966656
        1989: [6.4892672947328585, 49.451626875054174]
        0:6.4892672947328585
        1:49.451626875054174
        1990: [6.503679135475568, 49.449379483821936]
        0:6.503679135475568
        1:49.449379483821936
        1991: [6.523379457147615, 49.43663988036973]
        0:6.523379457147615
        1:49.43663988036973
        1992: [6.529492893289945, 49.4359665531076]
        0:6.529492893289945
        1:49.4359665531076
        1993: [6.541157942552238, 49.43467784893272]
        0:6.541157942552238
        1:49.43467784893272
        1994: [6.543681711684472, 49.43441032385714]
        0:6.543681711684472
        1:49.43441032385714
        1995: [6.55156988914428, 49.42590235244993]
        0:6.55156988914428
        1:49.42590235244993
        1996: [6.555311069764584, 49.42187134132668]
        0:6.555311069764584
        1:49.42187134132668
        1997: [6.548689285049581, 49.41751029610995]
        0:6.548689285049581
        1:49.41751029610995
        1998: [6.548216613947353, 49.417198175283986]
        0:6.548216613947353
        1:49.417198175283986
        1999: [6.545211212426947, 49.4152183455856]
        0:6.545211212426947
        1:49.4152183455856
        2000: [6.542798849359196, 49.41017955872409]
        0:6.542798849359196
        1:49.41017955872409
        2001: [6.543472168378483, 49.40612179573049]
        0:6.543472168378483
        1:49.40612179573049
        2002: [6.543891291127856, 49.40356220638451]
        0:6.543891291127856
        1:49.40356220638451
        2003: [6.54389130607065, 49.403557768546555]
        0:6.54389130607065
        1:49.403557768546555
        2004: [6.5526489616017285, 49.399700626288926]
        0:6.5526489616017285
        1:49.399700626288926
        2005: [6.56669959485497, 49.38944916615462]
        0:6.56669959485497
        1:49.38944916615462
        2006: [6.5716001984914, 49.38808915831976]
        0:6.5716001984914
        1:49.38808915831976
        2007: [6.575479562083136, 49.38826749289897]
        0:6.575479562083136
        1:49.38826749289897
        2008: [6.58244027404597, 49.38857962892068]
        0:6.58244027404597
        1:49.38857962892068
        2009: [6.58734975151216, 49.38585068611582]
        0:6.58734975151216
        1:49.38585068611582
        2010: [6.589820109366117, 49.381280117465636]
        0:6.589820109366117
        1:49.381280117465636
        2011: [6.588451156866392, 49.37602285217128]
        0:6.588451156866392
        1:49.37602285217128
        2012: [6.588451147451036, 49.37601833975951]
        0:6.588451147451036
        1:49.37601833975951
        2013: [6.592696154300623, 49.37311105045161]
        0:6.592696154300623
        1:49.37311105045161
        2014: [6.601079318283527, 49.367372163947465]
        0:6.601079318283527
        1:49.367372163947465
        2015: [6.602345667824143, 49.36471009113487]
        0:6.602345667824143
        1:49.36471009113487
        2016: [6.602488342973018, 49.364397982754724]
        0:6.602488342973018
        1:49.364397982754724
        2017: [6.599772723485805, 49.35991206190119]
        0:6.599772723485805
        1:49.35991206190119
        2018: [6.599028108806831, 49.35868140180579]
        0:6.599028108806831
        1:49.35868140180579
        2019: [6.591041864493092, 49.35089134050497]
        0:6.591041864493092
        1:49.35089134050497
        2020: [6.579818256017018, 49.35795007384597]
        0:6.579818256017018
        1:49.35795007384597
        2021: [6.572139724736477, 49.35770040825016]
        0:6.572139724736477
        1:49.35770040825016
        2022: [6.570449735231052, 49.34855920715439]
        0:6.570449735231052
        1:49.34855920715439
        2023: [6.571818670360389, 49.34373891979642]
        0:6.571818670360389
        1:49.34373891979642
        2024: [6.5725900666046515, 49.34101886816958]
        0:6.5725900666046515
        1:49.34101886816958
        2025: [6.591840000909414, 49.33330020799203]
        0:6.591840000909414
        1:49.33330020799203
        2026: [6.592682770219486, 49.33176623583346]
        0:6.592682770219486
        1:49.33176623583346
        2027: [6.595358304982192, 49.32691026461702]
        0:6.595358304982192
        1:49.32691026461702
        2028: [6.590859029362227, 49.32073000486772]
        0:6.590859029362227
        1:49.32073000486772
        2029: [6.591220202119771, 49.31981138318075]
        0:6.591220202119771
        1:49.31981138318075
        2030: [6.591929226923406, 49.31799211734296]
        0:6.591929226923406
        1:49.31799211734296
        2031: [6.614340619886462, 49.303419727073404]
        0:6.614340619886462
        1:49.303419727073404
        2032: [6.628658857544073, 49.299790022296804]
        0:6.628658857544073
        1:49.299790022296804
        2033: [6.645438444957705, 49.2925083367886]
        0:6.645438444957705
        1:49.2925083367886
        2034: [6.6487114592077505, 49.2896009965178]
        0:6.6487114592077505
        1:49.2896009965178
        2035: [6.6569919219165365, 49.28223902043556]
        0:6.6569919219165365
        1:49.28223902043556
        2036: [6.666561161601169, 49.28065159527662]
        0:6.666561161601169
        1:49.28065159527662
        2037: [6.669209880661101, 49.28021011499124]
        0:6.669209880661101
        1:49.28021011499124
        2038: [6.669530971924812, 49.27787802612987]
        0:6.669530971924812
        1:49.27787802612987
        2039: [6.67028005969036, 49.27243791014141]
        0:6.67028005969036
        1:49.27243791014141
        2040: [6.664411867753193, 49.259181032031286]
        0:6.664411867753193
        1:49.259181032031286
        2041: [6.666868893993182, 49.25483787128739]
        0:6.666868893993182
        1:49.25483787128739
        2042: [6.6849995716884685, 49.25440979629941]
        0:6.6849995716884685
        1:49.25440979629941
        2043: [6.688500014559011, 49.25076223680012]
        0:6.688500014559011
        1:49.25076223680012
        2044: [6.688450938222081, 49.249593962059755]
        0:6.688450938222081
        1:49.249593962059755
        2045: [6.6884196884362845, 49.24892954595669]
        0:6.6884196884362845
        1:49.24892954595669
        2046: [6.688330533286896, 49.24668660200426]
        0:6.688330533286896
        1:49.24668660200426
        2047: [6.688178917419386, 49.24275812579195]
        0:6.688178917419386
        1:49.24275812579195
        2048: [6.6861188161463, 49.2349903417557]
        0:6.6861188161463
        1:49.2349903417557
        2049: [6.691278025015065, 49.21867898662131]
        0:6.691278025015065
        1:49.21867898662131
        2050: [6.691750657720209, 49.21718075355193]
        0:6.691750657720209
        1:49.21718075355193
        2051: [6.693311309160608, 49.21625320470293]
        0:6.693311309160608
        1:49.21625320470293
        2052: [6.695589940945786, 49.21490215033549]
        0:6.695589940945786
        1:49.21490215033549
        2053: [6.696856322758202, 49.21482628485925]
        0:6.696856322758202
        1:49.21482628485925
        2054: [6.7032507004969935, 49.214460653578875]
        0:6.7032507004969935
        1:49.214460653578875
        2055: [6.715771882079173, 49.22018172883727]
        0:6.715771882079173
        1:49.22018172883727
        2056: [6.722389170517314, 49.2206409768211]
        0:6.722389170517314
        1:49.2206409768211
        2057: [6.725880589094973, 49.21653858592756]
        0:6.725880589094973
        1:49.21653858592756
        2058: [6.727142491724462, 49.214126208201016]
        0:6.727142491724462
        1:49.214126208201016
        2059: [6.731137885884669, 49.206487780308]
        0:6.731137885884669
        1:49.206487780308
        2060: [6.719584400391554, 49.19333792999022]
        0:6.719584400391554
        1:49.19333792999022
        2061: [6.715869893004351, 49.189110706390345]
        0:6.715869893004351
        1:49.189110706390345
        2062: [6.7244269089186774, 49.17805655764485]
        0:6.7244269089186774
        1:49.17805655764485
        2063: [6.732141222994499, 49.16809047452649]
        0:6.732141222994499
        1:49.16809047452649
        2064: [6.739761761796436, 49.16762674557858]
        0:6.739761761796436
        1:49.16762674557858
        2065: [6.7465485569958945, 49.16721200180257]
        0:6.7465485569958945
        1:49.16721200180257
        2066: [6.754115600233835, 49.16735916878335]
        0:6.754115600233835
        1:49.16735916878335
        2067: [6.758378563316327, 49.16743943289838]
        0:6.758378563316327
        1:49.16743943289838
        2068: [6.762124221877494, 49.1659099657805]
        0:6.762124221877494
        1:49.1659099657805
        2069: [6.766734924307353, 49.16402380112355]
        0:6.766734924307353
        1:49.16402380112355
        2070: [6.766739351403216, 49.1640193539915]
        0:6.766739351403216
        1:49.1640193539915
        2071: [6.7702085819109215, 49.16768023562522]
        0:6.7702085819109215
        1:49.16768023562522
        2072: [6.771992167288945, 49.167974529296906]
        0:6.771992167288945
        1:49.167974529296906
        2073: [6.782729682133064, 49.169740368477065]
        0:6.782729682133064
        1:49.169740368477065
        2074: [6.794559669872863, 49.167689184001354]
        0:6.794559669872863
        1:49.167689184001354
        2075: [6.823789146714482, 49.157188005338114]
        0:6.823789146714482
        1:49.157188005338114
        2076: [6.830825544590707, 49.155270549935054]
        0:6.830825544590707
        1:49.155270549935054
        2077: [6.836310271367236, 49.153767826856935]
        0:6.836310271367236
        1:49.153767826856935
        2078: [6.8390883079898535, 49.15582793078842]
        0:6.8390883079898535
        1:49.15582793078842
        2079: [6.843609811124454, 49.1601710867322]
        0:6.843609811124454
        1:49.1601710867322
        2080: [6.847194946930267, 49.17107804664961]
        0:6.847194946930267
        1:49.17107804664961
        2081: [6.847440194138195, 49.171818293173665]
        0:6.847440194138195
        1:49.171818293173665
        2082: [6.852474457781788, 49.17313822170722]
        0:6.852474457781788
        1:49.17313822170722
        2083: [6.861361472465937, 49.175470303383634]
        0:6.861361472465937
        1:49.175470303383634
        2084: [6.862329154805899, 49.18291702731735]
        0:6.862329154805899
        1:49.18291702731735
        2085: [6.862400433809059, 49.183469946314155]
        0:6.862400433809059
        1:49.183469946314155
        2086: [6.8605766425604795, 49.186163230935044]
        0:6.8605766425604795
        1:49.186163230935044
        2087: [6.859609095166335, 49.187590140406115]
        0:6.859609095166335
        1:49.187590140406115
        2088: [6.854391897485975, 49.19969216345114]
        0:6.854391897485975
        1:49.19969216345114
        2089: [6.84251287435662, 49.20822686339766]
        0:6.84251287435662
        1:49.20822686339766
        2090: [6.839770546332307, 49.21019776283712]
        0:6.839770546332307
        1:49.21019776283712
        2091: [6.844621993133002, 49.21386763856139]
        0:6.844621993133002
        1:49.21386763856139
        2092: [6.846731138657782, 49.21545947845295]
        0:6.846731138657782
        1:49.21545947845295
        2093: [6.852300617814013, 49.2197982312522]
        0:6.852300617814013
        1:49.2197982312522
        2094: [6.856130948055307, 49.21979819419182]
        0:6.856130948055307
        1:49.21979819419182
        2095: [6.865022388126, 49.219606464502846]
        0:6.865022388126
        1:49.219606464502846
        2096: [6.866578656494359, 49.219570841632326]
        0:6.866578656494359
        1:49.219570841632326
        2097: [6.880161042558104, 49.214081648780976]
        0:6.880161042558104
        1:49.214081648780976
        2098: [6.886078306456597, 49.21178963921576]
        0:6.886078306456597
        1:49.21178963921576
        2099: [6.8906711861938925, 49.21143292890445]
        0:6.8906711861938925
        1:49.21143292890445
        2100: [6.89479135878046, 49.2111119128554]
        0:6.89479135878046
        1:49.2111119128554
        2101: [6.917778033155353, 49.21817954514973]
        0:6.917778033155353
        1:49.21817954514973
        2102: [6.922651841819583, 49.22090852521984]
        0:6.922651841819583
        1:49.22090852521984
        2103: [6.927200086802815, 49.22104678187642]
        0:6.927200086802815
        1:49.22104678187642
        2104: [6.930321491707041, 49.221140373246584]
        0:6.930321491707041
        1:49.221140373246584
        2105: [6.937384683788145, 49.21881278420113]
        0:6.937384683788145
        1:49.21881278420113
        2106: [6.940060164530395, 49.21792983299333]
        0:6.940060164530395
        1:49.21792983299333
        2107: [6.945638499109583, 49.214848598244615]
        0:6.945638499109583
        1:49.214848598244615
        2108: [6.957459526868915, 49.208320496487225]
        0:6.957459526868915
        1:49.208320496487225
        2109: [6.967782352981461, 49.20773188989942]
        0:6.967782352981461
        1:49.20773188989942
        2110: [6.977998211265628, 49.20715223901716]
        0:6.977998211265628
        1:49.20715223901716
        2111: [6.984548653377765, 49.20439202124028]
        0:6.984548653377765
        1:49.20439202124028
        2112: [6.986689014149483, 49.203491315426156]
        0:6.986689014149483
        1:49.203491315426156
        2113: [6.9866934330849615, 49.20348685618716]
        0:6.9866934330849615
        1:49.20348685618716
        2114: [6.987576373652167, 49.202849180967846]
        0:6.987576373652167
        1:49.202849180967846
        2115: [7.000588026869157, 49.193418159849024]
        0:7.000588026869157
        1:49.193418159849024
        2116: [7.008003463641624, 49.193614349413814]
        0:7.008003463641624
        1:49.193614349413814
        2117: [7.008596558941998, 49.193627719958855]
        0:7.008596558941998
        1:49.193627719958855
        2118: [7.00860100203266, 49.1936321838066]
        0:7.00860100203266
        1:49.1936321838066
        2119: [7.0131759909559905, 49.19061340184679]
        0:7.0131759909559905
        1:49.19061340184679
        2120: [7.014848245659399, 49.18951202604453]
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    - properties: Object (8 properties)
      - ADM0_CODE:93
      - ADM0_NAME:Germany
      - DISP_AREA:NO
      - EXP0_YEAR:3000
      - STATUS:Member State
      - STR0_YEAR:1000
      - Shape_Area:45.8756542925
      - Shape_Leng:79.753405432

A visualization always uses these steps: (1) Instantiate a map object, (2) add desired layers, (3) call the map object. Important: only when the map object is called, there will be any calculation started on the server-side! In the code below you will notice the empty {}. These need to be defined for the vis_params which are the general visualization parameters for an individual feature. Since we are defining it here via the style for the entire feature collection, we need to leave it empty. To showcase the difference, I will also visualize the polygon from above (two important things here: (1) with folium one can only visualize ee.FeatureCollections() but not ee.Features or ee.Geometries, so I will have to convert polygon_feat into a feature collection before visualizing it, (2) the polygon is located in South America, so you will have to pan to South America in order to see it).

map01 = geemap.Map()
map01.centerObject(ger, 6) # this makes sure that we are centered on the feature collection
style = {"color": "0000ffff", "width": 2, "lineType": "solid", "fillColor": "FF000080"} # Define the style for the feature collection
map01.addLayer(ger.style(**style), {}, 'Germany')
map01.addLayer(ee.FeatureCollection([polygon_feat]), {"color": "red"}, 'Polygon in South America') # Conver the feature into a feature collection first
map01

Raster Data#

The other prominent data type in GEE (and in our course) are raster data. Similar to what we have already known from our course. In GEE, and image is defined as ee.Image() and consists of bands and a dictionary of properties. There is a huge database of image data, that is growing at a daily basis, containing both satellite data (e.g., Sentinel, Landsat), but also entire dataproducts. Check out the Google Earth Engine Data Catalog and the website Awesome GEE datasets for the wide section of datasets. Have these sources also in mind when you work on your final class project.

Similar to vector data, multiple raster images can be combined inside so-called ee.ImageCollections(), to which we can apply filters etc. Below you find some examples. First, I will load some individual images from the data catalogues referenced above, and visualize them (you will see that setting the visualization parameters is a bit non-intuitive). Second, I will load an ee.ImageCollection() and apply some filters to it. In both cases, we will use the polygon_geom from above:

# Load some images. all of them are clipped to the polygon_geom
tc = ee.Image("UMD/hansen/global_forest_change_2023_v1_11").select(['treecover2000']).clip(polygon_geom)
lossYR = ee.Image("UMD/hansen/global_forest_change_2023_v1_11").select(['lossyear']).clip(polygon_geom)
esaWC21 = ee.ImageCollection("ESA/WorldCover/v200").select(['Map']).toBands().clip(polygon_geom)
tri = ee.ImageCollection("projects/sat-io/open-datasets/Geomorpho90m/tri").median().clip(polygon_geom) # terrain ruggedness. Check out awesone GEE datasets for examples on how to load the data

# Instantiate the map and set the center, we also add a satellite basemap underneath
map02 = geemap.Map()
map02.centerObject(ee.FeatureCollection([polygon_feat]), 6)
map02.add_basemap('SATELLITE')
# Load the layers
map02.addLayer(tc, {'palette': ['000000', '00FF00'], 'min': 0, 'max': 100}, 'Tree cover 2000')
map02.addLayer(lossYR, {'palette': ['FF0000', '000000'], 'min': 0, 'max': 23}, 'Loss year')
#map02.addLayer(esaWC21, {'min': 10,'max': 100, 'palette': ['006400','ffbb22','ffff4c','f096ff','fa0000','b4b4b4','f0f0f0','0064c8','0096a0','00cf75','fae6a0']}, 'ESA World Cover 21') # colors were taken from the website
map02.addLayer(tri, {'min': 0, 'max': 1, 'palette': ['000000', 'FFFFFF']}, 'Terrain ruggedness')
# Call the map object
map02

Now, after having looked at individual images, we now want to shift our focus to collections of images. In our own research, the most common image collections are satellite archives. Thereby, a collection of e.g., Landsat images contains all images of a particular sensor (e.g., OLI and OLI2 on board of Landsat 8 & 9) globally that have ever been taken. Currently, ~580,000 images are taken each year - quite a substantial collection. Obviously before we start working with these, we need to apply some spatial and temporal filters. Below you find some examples for such filters. We take the collections of Landsat 8 and 9 over our polygon_geom in South America:

l8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filterBounds(polygon_geom)\
.filter(ee.Filter.date(ee.Date.fromYMD(2024, 1, 1), ee.Date.fromYMD(2024, 12, 31))) # here we filter by the date taking all images acquired during 2024

l9 = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2").filterBounds(polygon_geom)\
.filter(ee.Filter.date(ee.Date.fromYMD(2024, 1, 1), ee.Date.fromYMD(2024, 12, 31))) # here we filter by the date taking all images acquired during 2024

all_images = l8.merge(l9) # merge the two collections
all_images.size()

183

During 2024, 183 images were taken over our small area in South America; representing a solid database to work with. We can now filter further, by allowing only a certain cloud coverage, let’s say a maximum of 20%. This information is stored in the attribute CLOUD_COVER (see here):

all_images_lt20 = all_images.filter(ee.Filter.lte('CLOUD_COVER', 20))
all_images_lt20.size()

111

Already a lot less, but still a lot of data to work with. In subsequent sessions we will show you how to make use of the data, such as removing clouds, calculating spectral-temporal metrics, etc. For now, we leave it at this!

Other data types#

Similar to what we have introduced in our introduction to python, there are other important data types in GEE, which we name here briefly:

Data Type	Example
Dictionaries	`ee.Dictionary({'e': np.e, 'pi': np.pi})`
Lists	`ee.List([1, 2, 3, 4, 5])`
Dates	`ee.Date('2022-12-06')` or `ee.Date.fromYMD(2017, 1, 13)`
Numbers	`ee.Number(1)` or `ee.Number(np.pi)`
Strings	`ee.String("We love geopy")`

And that is it. Pretty cool! Although we haven’t done any analysis, I hope that this shows to you already how powerful GEE can be simply for visualizations and some simple selections. What years ago meant (a) downloading a bunch of files, (b) loading them into QGIS, can now be done in just a few seconds. In addition, using the tools in the window, we can now also do some simple digitizations, etc. Check out the different datasets yourself to see the richness of the available data. Try to build yourself a quick script that will allow you to quickly examine any region of the world in just a few seconds - a script that you are able to call whenever you want.