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Illustration by Creator
Geospatial knowledge evaluation is a area addressed to take care of, visualize and analyze a particular kind of information, known as geospatial knowledge. In comparison with the traditional knowledge, we’ve got tabular knowledge with a further column, the placement data, akin to latitude and longitude.
There are two essential kinds of knowledge: vector knowledge and raster knowledge. When coping with vector knowledge, you continue to have a tabular dataset, whereas raster knowledge are extra just like pictures, akin to satellite tv for pc pictures and aerial pictures.
On this article, I’m going to give attention to raster knowledge supplied by Google Earth Engine, a cloud computing platform that gives an enormous knowledge catalog of satellite tv for pc imagery. This type of knowledge will be simply mastered out of your Jupyter Pocket book utilizing a life-saving Python package deal, known as Geemap. Let’s get began!
What’s Google Earth Engine?
Screenshot by Creator. Dwelling web page of Google Earth Engine.
Earlier than getting began with the Python Library, we have to perceive the potential of Google Earth Engine. This cloud-based platform, powered by Google Cloud platform, hosts public and free geospatial datasets for tutorial, non-profit and enterprise functions.
Screenshot by Creator. Overview of Earth Engine Knowledge Catalog.
The fantastic thing about this platform is that it offers a multi-petabyte catalog of raster and vector knowledge, saved on the Earth Engine servers. You may have a quick overview from this link. Furthermore, it offers APIs to facilitate the evaluation of raster datasets.
What’s Geemap?
Illustration by Creator. Geemap library.
Geemap is a Python library that enables to research and visualize enormous quantities of geospatial knowledge from Google Earth Engine.
Earlier than this package deal, it was already attainable to make computational requests by means of JavaScript and Python APIs, however Python APIs had restricted functionalities and lacked documentation.
To fill this hole, Geemap was created to allow customers to entry assets of Google Earth Engine with few strains of code. Geemap is constructed upon eartengine-api, ipyleaflet and folium.
To put in the library, you simply want the next command:
I like to recommend you experiment with this superb package deal in Google Colab to grasp its full potential. Check out this free book written by professor Dr. Qiusheng Wu for getting began with Geemap and Google Earth Engine.
Find out how to Entry Earth Engine?
First, we have to import two Python libraries, that will likely be used throughout the tutorial:
Along with geemap, we’ve got imported the Earth Engine Python shopper library, known as ee.
This Python library will be utilized for the authentication on Earth Engine, however it may be quicker through the use of straight the Geemap library:
It’s good to click on the URL returned by this line of code, which can generate the authorization code. First, choose the cloud venture and, then, click on the “GENERATE TOKEN” button.
Screenshot by Creator. Pocket book Authenticator.
After, it would ask you to decide on the account. I like to recommend taking the identical account of Google Colab in case you are utilizing it.
Screenshot by Creator. Select an account.
Then, click on the test field subsequent to Choose All and press the “Proceed” button. In a nutshell, this step permits the Pocket book Shopper to entry the Earth Engine account.
Screenshot by Creator. Enable the Pocket book Shopper to entry your Earth Engine account.
After this motion, the authentication code is generated and you may paste it into the pocket book cell.
Screenshot by Creator. Copy the Authentication Code.
As soon as the verification code is entered, you possibly can lastly create and visualize this interactive map:
For now, you’re simply observing the bottom map on high of ipyleaflet, a Python package deal that permits the visualization of interactive maps throughout the Jupyter Pocket book.
Create Interactive Maps
Beforehand, we’ve got seen how one can authenticate and visualize an interactive map utilizing a single line of code. Now, we are able to customise the default map by specifying the latitude and longitude of the centroid, the extent of zoom and the peak. I’ve chosen the coordinates of Rome for the centre to give attention to the map of Europe.
m = geemap.Map(middle=[41, 12], zoom=6, peak=600)
m
If we need to change the bottom map, there are two attainable methods. The primary approach consists of writing and working the next code line:
m.add_basemap("ROADMAP")
m
Alternatively, you possibly can change manually the bottom map by clicking the icon of ring spanner positioned on the proper.
Furthermore, we see the listing of base maps supplied by Geemap:
basemaps = geemap.basemaps.keys()
for bm in basemaps:
print(bm)
That is the output:
OpenStreetMap
Esri.WorldStreetMap
Esri.WorldImagery
Esri.WorldTopoMap
FWS NWI Wetlands
FWS NWI Wetlands Raster
NLCD 2021 CONUS Land Cowl
NLCD 2019 CONUS Land Cowl
...
As you possibly can discover, there’s a lengthy sequence of base maps, most of them accessible because of OpenStreetMap, ESRI and USGS.
Earth Engine Knowledge Sorts
Earlier than exhibiting the complete potential of Geemap, it’s vital to know two essential knowledge sorts in Earth Engine. Check out the Google Earth Engine’s documentation for extra particulars.
Illustration by Creator. Instance of vector knowledge sorts: Geometry, Function and FeatureCollection.
When dealing with vector knowledge, we use principally three knowledge sorts:
- Geometry shops the coordinates wanted to attract the vector knowledge on a map. Three essential kinds of geometries are supported by Earth Engine: Level, LineString and Polygon.
- Function is actually a row that mixes geometry and non-geographical attributes. It’s similar to the GeoSeries class of GeoPandas.
- FeatureCollection is a tabular knowledge construction that comprises a set of options. FeatureCollection and GeoDataFrame are nearly similar conceptually.
Screenshot by Creator. Instance of Picture knowledge kind. It reveals the Australian Smoothed Digital Elevation Mannequin (DEM-S)
On this planet of raster knowledge, we give attention to Picture objects. Google Earth Engine’s Photographs are composed of a number of manufacturers, the place every band has a selected title, estimated minimal and most, and outline.
If we’ve got a group or time sequence of pictures, ImageCollection is extra acceptable as an information kind.
Screenshot by Creator. Copernicus CORINE Land Cover.
We visualize the satellite tv for pc imagery exhibiting the land cowl map of Europe. This dataset offers the modifications between 1986 and 2018.
First, we load the picture utilizing ee.Picture and, then, choose the band “landcover”. Lastly, let’s visualize the picture by including the loaded dataset to the map as a layer utilizing Map.addLayer.
Map = geemap.Map()
dataset = ee.Picture('COPERNICUS/CORINE/V20/100m/2012')
landCover = dataset.choose('landcover')
Map.setCenter(16.436, 39.825, 6)
Map.addLayer(landCover, {}, 'Land Cowl')
Map
Screenshot by Creator.
Equally, we are able to do the identical factor to load and visualize the satellite tv for pc imagery exhibiting the land cowl map of Europe. This dataset offers the modifications between 1986 and 2018.
Screenshot by Creator. Offline high-resolution imagery of methane concentrations.
To visualise an Earth Engine ImageCollection, the strains of code are related, aside from ee.ImageCollection.
Map = geemap.Map()
assortment = ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4').choose('CH4_column_volume_mixing_ratio_dry_air').filterDate('2019-06-01', '2019-07-16')
band_viz = {
'min': 1750,
'max': 1900,
'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']
}
Map.addLayer(assortment.imply(), band_viz, 'S5P CH4')
Map.setCenter(0.0, 0.0, 2)
Map
Screenshot by Creator.
That’s nice! From this map, we discover how Methane, one of the vital vital contributors to the greenhouse impact, is distributed throughout the globe.
Closing Ideas
This was an introductory information that may enable you work with Google Earth Engine knowledge utilizing Python. Geemap is essentially the most full Python library to visualise and analyze this kind of knowledge.
If you wish to go deeper into this package deal, you possibly can check out the assets I advised beneath.
The code will be discovered here. I hope you discovered the article helpful. Have a pleasant day!
Helpful assets:
Eugenia Anello is presently a analysis fellow on the Division of Data Engineering of the College of Padova, Italy. Her analysis venture is concentrated on Continuous Studying mixed with Anomaly Detection.
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